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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection (2407.15369v1)

Published 22 Jul 2024 in cs.CV

Abstract: Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method's effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://github.com/GrokCV/SDD.

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References (63)
  1. H. Deng, X. Sun, and X. Zhou, “A multiscale fuzzy metric for detecting small infrared targets against chaotic cloudy/sea-sky backgrounds,” IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1694–1707, 2019.
  2. X. Bai and Y. Bi, “Derivative entropy-based contrast measure for infrared small-target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 4, pp. 2452–2466, 2018.
  3. H. Deng, X. Sun, M. Liu, C. Ye, and X. Zhou, “Small infrared target detection based on weighted local difference measure,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7, pp. 4204–4214, 2016.
  4. H. Deng and Y. Zhang, “FMR-YOLO: Infrared ship rotating target detection based on synthetic fog and multiscale weighted feature fusion,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–17, 2024.
  5. R. Usamentiaga, Y. Mokhtari, C. Ibarra-Castanedo, M. Klein, M. Genest, and X. Maldague, “Automated dynamic inspection using active infrared thermography,” IEEE Transactions on Industrial Informatics, vol. 14, no. 12, pp. 5648–5657, 2018.
  6. M. Zhang, R. Zhang, Y. Yang, H. Bai, J. Zhang, and J. Guo, “ISNet: Shape matters for infrared small target detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 867–876.
  7. M. Yang and X. Fan, “YOLOv8-Lit: A lightweight object detection model for real-time autonomous driving systems,” IECE Transactions on Emerging Topics in Artificial Intelligence, vol. 1, no. 1, pp. 1–16, 2024.
  8. Z. Huang, P. Zhang, R. Liu, and D. Li, “An improved yolov3-based method for immature apple detection,” IECE Transactions on Internet of Things, vol. 1, no. 1, pp. 9–14, 2023.
  9. Y. Dai, Y. Wu, F. Zhou, and K. Barnard, “Asymmetric contextual modulation for infrared small target detection,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 949–958.
  10. H. Wang, L. Zhou, and L. Wang, “Miss Detection vs. False Alarm: Adversarial learning for small object segmentation in infrared images,” in IEEE International Conference on Computer Vision (ICCV), 2019, pp. 8508–8517.
  11. H. Fang, L. Ding, L. Wang, Y. Chang, L. Yan, and J. Han, “Infrared small uav target detection based on depthwise separable residual dense network and multiscale feature fusion,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–20, 2022.
  12. Y. Dai, Y. Wu, F. Zhou, and K. Barnard, “Attentional local contrast networks for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 11, pp. 9813–9824, 2021.
  13. F. Wu, T. Zhang, L. Li, Y. Huang, and Z. Peng, “RPCANet: Deep unfolding RPCA based infrared small target detection,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2024, pp. 4809–4818.
  14. Y. Dai, X. Li, F. Zhou, Y. Qian, Y. Chen, and J. Yang, “One-stage cascade refinement networks for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–17, 2023.
  15. Z. Yang, T. Ma, Y. Ku, Q. Ma, and J. Fu, “DFFIR-net: Infrared dim small object detection network constrained by gray-level distribution model,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–15, 2022.
  16. C. Gao, D. Meng, Y. Yang, Y. Wang, X. Zhou, and A. G. Hauptmann, “Infrared patch-image model for small target detection in a single image,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4996–5009, 2013.
  17. C. L. P. Chen, H. Li, Y. Wei, T. Xia, and Y. Y. Tang, “A local contrast method for small infrared target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 574–581, 2014.
  18. Y. Wei, X. You, and H. Li, “Multiscale patch-based contrast measure for small infrared target detection,” Pattern Recognition, vol. 58, pp. 216–226, 2016.
  19. X. Zhang, A. Wang, Z. Yan, S. Mazhar, and Y. Chang, “A detection method with antiinterference for infrared maritime small target,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3999–4014, 2024.
  20. J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 3, pp. 452–456, 2016.
  21. Y. Dai and Y. Wu, “Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 8, pp. 3752–3767, 2017.
  22. X. Wang, Z. Peng, D. Kong, and Y. He, “Infrared dim and small target detection based on stable multisubspace learning in heterogeneous scene,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 10, pp. 5481–5493, 2017.
  23. D. Pang, T. Shan, W. Li, P. Ma, S. Liu, and R. Tao, “Infrared dim and small target detection based on greedy bilateral factorization in image sequences,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3394–3408, 2020.
  24. Y. Li, Y. Zhang, J. G. Yu, Y. Tan, J. Tian, and J. Ma, “A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences,” Information Sciences, p. S0020025516305230, 2016.
  25. J. Guo, Y. Wu, and Y. Dai, “Small target detection based on reweighted infrared patch-image model,” IET Image Processing, vol. 12, no. 1, pp. 70–79, 2017.
  26. Y. Dai, Y. Wu, Y. Song, and J. Guo, “Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values,” Infrared Physics and Technology, vol. 81, pp. 182–194, 2017.
  27. C. Gao, L. Wang, Y. Xiao, Q. Zhao, and D. Meng, “Infrared small-dim target detection based on markov random field guided noise modeling,” Pattern Recognition, vol. 76, pp. 463–475, 2017.
  28. H. Zhu, S. Liu, L. Deng, Y. Li, and F. Xiao, “Infrared small target detection via low-rank tensor completion with top-hat regularization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 2, pp. 1004–1016, 2020.
  29. F. Zhou, Y. Wu, and Y. Dai, “Infrared small target detection via incorporating spatial structural prior into intrinsic tensor sparsity regularization,” Digital Signal Processing, vol. 111, no. 5, p. 102966, 2021.
  30. Y. Sun, J. Yang, and W. An, “Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–16, 2020.
  31. H. Liu, L. Zhang, and H. Huang, “Small target detection in infrared videos based on spatio-temporal tensor model,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–12, 2020.
  32. F. Zhou, M. Fu, Y. Duan, Y. Dai, and Y. Wu, “Infrared small target detection via l0subscript𝑙0l_{0}italic_l start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT sparse gradient regularized tensor spectral support low-rank decomposition,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 3, pp. 2105–2122, 2023.
  33. L. Deng, D. Xu, G. Xu, and H. Zhu, “A generalized low-rank double-tensor nuclear norm completion framework for infrared small target detection,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 4, pp. 3297–3312, 2022.
  34. J. Li, P. Zhang, L. Zhang, and Z. Zhang, “Sparse regularization-based spatial-temporal twist tensor model for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–17, 2023.
  35. B. Dan, Z. Zhu, X. Qi, J. Zhang, Y. Ouyang, M. Li, and T. Tang, “Dynamic weight-guided smooth-sparse decomposition for small target detection against strong vignetting background,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–15, 2024.
  36. P. Zhang, L. Zhang, X. Wang, F. Shen, T. Pu, and C. Fei, “Edge and corner awareness-based spatial-temporal tensor model for infrared small-target detection,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–17, 2020.
  37. T. Liu, J. Yang, B. Li, C. Xiao, Y. Sun, Y. Wang, and W. An, “Nonconvex tensor low-rank approximation for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2022.
  38. F. Wu, H. Yu, A. Liu, J. Luo, and Z. Peng, “Infrared small target detection using spatiotemporal 4-D tensor train and ring unfolding,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–22, 2023.
  39. Y. Liu, X. Liu, X. Hao, W. Tang, S. Zhang, and T. Lei, “Single-frame infrared small target detection by high local variance, low-rank and sparse decomposition,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–17, 2023.
  40. Q. Lu, Q. Li, L. Hu, and L. Huang, “An effective low-contrast sf6 gas leakage detection method for infrared imaging,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2021.
  41. Y. Xu, M. Wan, X. Zhang, J. Wu, Y. Chen, Q. Chen, and G. Gu, “Infrared small target detection based on local contrast-weighted multidirectional derivative,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–16, 2023.
  42. D. Liu, L. Cao, Z. Li, T. Liu, and P. Che, “Infrared small target detection based on flux density and direction diversity in gradient vector field,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 7, pp. 2528–2554, 2018.
  43. J. Liu, J. Zhang, Y. Wei, and L. Zhang, “Infrared small target detection based on multidirectional gradient,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023.
  44. Y. Qin, L. Bruzzone, C. Gao, and B. Li, “Infrared small target detection based on facet kernel and random walker,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 7104–7118, 2019.
  45. D. Pang, P. Ma, T. Shan, W. Li, R. Tao, Y. Ma, and T. Wang, “STTM-SFR: Spatial-temporal tensor modeling with saliency filter regularization for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2022.
  46. T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review, vol. 51, no. 3, pp. 455–500, 2009.
  47. M. Zhao, W. Li, L. Li, J. Hu, P. Ma, and R. Tao, “Single-frame infrared small-target detection: A survey,” IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 2, pp. 87–119, 2022.
  48. L. Zhang and Z. Peng, “Infrared small target detection based on partial sum of the tensor nuclear norm,” Remote Sensing, vol. 11, no. 4, p. 382, 2019.
  49. X. Kong, C. Yang, S. Cao, C. Li, and Z. Peng, “Infrared small target detection via nonconvex tensor fibered rank approximation,” IEEE Transactions on Geoscience and Remote Sensing, vol. pp, no. 99, pp. 1–21, 2021.
  50. M. Zhao, W. Li, L. Li, P. Ma, Z. Cai, and R. Tao, “Three-order tensor creation and tucker decomposition for infrared small-target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022.
  51. L. Yang, P. Yan, M. Li, J. Zhang, and Z. Xu, “Infrared small target detection based on a group image-patch tensor model,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
  52. C. Zhang, Y. He, Q. Tang, Z. Chen, and T. Mu, “Infrared small target detection via interpatch correlation enhancement and joint local visual saliency prior,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022.
  53. G. Wang, B. Tao, X. Kong, and Z. Peng, “Infrared small target detection using nonoverlapping patch spatial-temporal tensor factorization with capped nuclear norm regularization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2022.
  54. Y. Chen, W. He, N. Yokoya, and T.-Z. Huang, “Hyperspectral image restoration using weighted group sparsity-regularized low-rank tensor decomposition,” IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3556–3570, 2020.
  55. S. Moradi, P. Moallem, and M. F. Sabahi, “Fast and robust small infrared target detection using absolute directional mean difference algorithm,” Signal Processing, vol. 177, p. 107727, 2020.
  56. Y. Li, Z. Li, Y. Shen, and J. Li, “Infrared small target detection based on 1-D difference of guided filtering,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023.
  57. Y.-B. Zheng, T.-Z. Huang, X.-L. Zhao, Y. Chen, and W. He, “Double-factor-regularized low-rank tensor factorization for mixed noise removal in hyperspectral image,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 12, pp. 8450–8464, 2020.
  58. V. B. S. Prasath, D. Vorotnikov, R. Pelapur, S. Jose, G. Seetharaman, and K. Palaniappan, “Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5220–5235, 2015.
  59. C. Vicas and S. Nedevschi, “Detecting curvilinear features using structure tensors,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3874–3887, 2015.
  60. A. Akl, C. Yaacoub, M. Donias, J.-P. Da Costa, and C. Germain, “Texture synthesis using the structure tensor,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4082–4095, 2015.
  61. P. Kirrinnis, “Fast algorithms for the sylvester equation ax- xbt= c,” Theoretical Computer Science, vol. 259, no. 1-2, pp. 623–638, 2001.
  62. S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al., “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning, vol. 3, no. 1, pp. 1–122, 2011.
  63. B. Hui, Z. Song, H. Fan, P. Zhong, W. Hu, X. Zhang, J. Ling, H. Su, W. Jin, Y. Zhang, and Y. Bai, “A dataset for infrared detection and tracking of dim-small aircraft targets under ground / air background,” Science Data Bank, vol. 5, no. 3, 2020.
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