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SI-FID: Only One Objective Indicator for Evaluating Stitched Images
Published 22 Apr 2024 in eess.IV | (2404.13905v1)
Abstract: Image quality evaluation accurately is vital in developing image stitching algorithms as it directly reflects the algorithms progress. However, commonly used objective indicators always produce inconsistent and even conflicting results with subjective indicators. To enhance the consistency between objective and subjective evaluations, this paper introduces a novel indicator the Frechet Distance for Stitched Images (SI-FID). To be specific, our training network employs the contrastive learning architecture overall. We employ data augmentation approaches that serve as noise to distort images in the training set. Both the initial and distorted training sets are then input into the pre-training model for fine-tuning. We then evaluate the altered FID after introducing interference to the test set and examine if the noise can improve the consistency between objective and subjective evaluation results. The rank correlation coefficient is utilized to measure the consistency. SI-FID is an altered FID that generates the highest rank correlation coefficient under the effect of a certain noise. The experimental results demonstrate that the rank correlation coefficient obtained by SI-FID is at least 25% higher than other objective indicators, which means achieving evaluation results closer to human subjective evaluation.
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IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. 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Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15(11), 3440–3451 (2006) https://doi.org/10.1109/TIP.2006.881959 Das et al. [2004] Das, K., Jiang, J., Rao, J.N.K.: Mean squared error of empirical predictor. The Annals of Statistics 32(2), 818–840 (2004) https://doi.org/10.1214/009053604000000201 Huynh-Thu and Ghanbari [2008] Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electronics letters 44(13), 800–801 (2008) https://doi.org/10.1049/el:20080522 Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Das, K., Jiang, J., Rao, J.N.K.: Mean squared error of empirical predictor. The Annals of Statistics 32(2), 818–840 (2004) https://doi.org/10.1214/009053604000000201 Huynh-Thu and Ghanbari [2008] Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electronics letters 44(13), 800–801 (2008) https://doi.org/10.1049/el:20080522 Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electronics letters 44(13), 800–801 (2008) https://doi.org/10.1049/el:20080522 Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. 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[2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. 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In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. 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[2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. 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IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electronics letters 44(13), 800–801 (2008) https://doi.org/10.1049/el:20080522 Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. 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[2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. 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In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. 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[2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004) https://doi.org/10.1109/TIP.2003.819861 Mittal et al. [2012] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. 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In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. 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IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012) https://doi.org/10.1109/TIP.2012.2214050 Mittal et al. [2013] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013) https://doi.org/10.1109/LSP.2012.2227726 Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. 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[2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. 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[2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. 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[2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. 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[2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629–6640 (2017). https://doi.org/10.5555/3295222.3295408 Hadsell et al. [2006] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. 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[2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. 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[2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100 Wu et al. [2018] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018). https://doi.org/10.1109/CVPR.2018.00393 Zhang et al. [2011] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. 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[2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. 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IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. 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IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011) https://doi.org/10.1109/TIP.2011.2109730 Qu et al. [2002] Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electronics letters 38(7), 1 (2002) Haghighat et al. [2011] Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. 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[2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. 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Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
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In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Computers & Electrical Engineering 37(5), 744–756 (2011) https://doi.org/10.1016/j.compeleceng.2011.07.012 . Special Issue on Image Processing Piella and Heijmans [2003] Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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[2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. 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In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, p. 173 (2003). https://doi.org/10.1109/ICIP.2003.1247209 Han et al. [2013] Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Information Fusion 14(2), 127–135 (2013) https://doi.org/10.1016/j.inffus.2011.08.002 Zhang et al. [2018] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068 Moorthy and Bovik [2010] Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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[2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17(5), 513–516 (2010) https://doi.org/10.1109/LSP.2010.2043888 N et al. [2015] N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. 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Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 N, V., D, P., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). https://doi.org/10.1109/NCC.2015.7084843 Madhusudana and Soundararajan [2019] Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. 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[2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Madhusudana, P.C., Soundararajan, R.: Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing 28(11), 5620–5635 (2019) https://doi.org/10.1109/TIP.2019.2921858 Zhou et al. [2017] Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. 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[2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zhou, X., Zhang, H., Wang, Y.: A multi-image stitching method and quality evaluation. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 46–50 (2017). https://doi.org/10.1109/ICISCE.2017.20 Cheung et al. [2017] Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Cheung, G., Yang, L., Tan, Z., Huang, Z.: A content-aware metric for stitched panoramic image quality assessment. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2487–249 (2017). https://doi.org/10.1109/ICCVW.2017.293 Ling et al. [2018] Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Ling, S., Cheung, G., Le Callet, P.: No-reference quality assessment for stitched panoramic images using convolutional sparse coding and compound feature selection. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. 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Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. 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In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486545 Wu et al. [2022] Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. [2016] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. 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In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Wu, C., Wu, F., Qi, T., Huang, Y.: Noisytune: A little noise can help you finetune pretrained language models better. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 680–685 (2022). https://doi.org/10.18653/v1/2022.acl-short.76 Szegedy et al. 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In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308 Chen and He [2021] Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Chen, X., He, K.: Exploring simple siamese representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15745–15753 (2021). https://doi.org/10.1109/CVPR46437.2021.01549 Nie et al. [2022] Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Nie, L., Lin, C., Liao, K., Liu, S., Zhao, Y.: Deep rectangling for image stitching: A learning baseline. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5730–5738 (2022). https://doi.org/10.1109/CVPR52688.2022.00565 Zaragoza et al. [2014] Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Zaragoza, J., Chin, T.-J., Tran, Q.-H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7), 1285–1298 (2014) https://doi.org/10.1109/TPAMI.2013.247 Chang et al. [2014] Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Chang, C.-H., Sato, Y., Chuang, Y.-Y.: Shape-preserving half-projective warps for image stitching. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254–3261 (2014). https://doi.org/10.1109/CVPR.2014.422 Lin et al. [2015] Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Lin, C.-C., Pankanti, S.U., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1155–1163 (2015). https://doi.org/10.1109/CVPR.2015.7298719 Liao and Li [2020] Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Transactions on Image Processing 29, 724–735 (2020) https://doi.org/10.1109/TIP.2019.2934344 Nahler [2009] Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Nahler, G.: correlation coefficient, pp. 40–41 (2009). https://doi.org/10.1007/978-3-211-89836-9-304 Jan and Tomasz [2011] Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1 Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
- Jan, H., Tomasz, K.: Comparison of values of pearson’s and spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87–93 (2011) https://doi.org/10.2478/v10117-011-0021-1
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