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Near-Real-Time Mueller Polarimetric Image Processing for Neurosurgical Intervention (2403.00893v1)

Published 1 Mar 2024 in eess.IV

Abstract: Wide-field imaging Mueller polarimetry is a revolutionary, label-free, and non-invasive modality for computer-aided intervention: in neurosurgery it aims to provide visual feedback of white matter fibre bundle orientation from derived parameters. Conventionally, robust polarimetric parameters are estimated after averaging multiple measurements of intensity for each pair of probing and detected polarised light. Long multi-shot averaging, however, is not compatible with real-time in-vivo imaging, and the current performance of polarimetric data processing hinders the translation to clinical practice. A learning-based denoising framework is tailored for fast, single-shot, noisy acquisitions of polarimetric intensities. Also, performance-optimised image processing tools are devised for the derivation of clinically relevant parameters. The combination recovers accurate polarimetric parameters from fast acquisitions with near-real-time performance, under the assumption of pseudo-Gaussian polarimetric acquisition noise. The denoising framework is trained, validated, and tested on experimental data comprising tumour-free and diseased human brain samples in different conditions. Accuracy and image quality indices showed significant improvements on testing data for a fast single-pass denoising versus the state-of-the-art and high polarimetric image quality standards. The computational time is reported for the end-to-end processing. The end-to-end image processing achieved real-time performance for a localised field of view. The denoised polarimetric intensities produced visibly clear directional patterns of neuronal fibre tracts in line with reference polarimetric image quality standards; directional disruption was kept in case of neoplastic lesions. The presented advances pave the way towards feasible oncological neurosurgical translations of novel, label free, interventional feedback.

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References (28)
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[2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ramella-Roman, J.C., Saytashev, I., Piccini, M.: A review of polarization-based imaging technologies for clinical and preclinical applications. J. Opt. 22(12), 123001 (2020) https://doi.org/10.1088/2040-8986/abbf8a Baba et al. [2002] Baba, J.S., Chung, J.-R., DeLaughter, A.H., Cameron, B.D., Cote, G.L.: Development and calibration of an automated Mueller matrix polarization imaging system. J. Biom. Optics 7(3), 341–349 (2002) https://doi.org/10.1117/1.1486248 Azzam [2016] Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Baba, J.S., Chung, J.-R., DeLaughter, A.H., Cameron, B.D., Cote, G.L.: Development and calibration of an automated Mueller matrix polarization imaging system. J. Biom. Optics 7(3), 341–349 (2002) https://doi.org/10.1117/1.1486248 Azzam [2016] Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? 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Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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  3. Baba, J.S., Chung, J.-R., DeLaughter, A.H., Cameron, B.D., Cote, G.L.: Development and calibration of an automated Mueller matrix polarization imaging system. J. Biom. Optics 7(3), 341–349 (2002) https://doi.org/10.1117/1.1486248 Azzam [2016] Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. 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IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  4. Azzam, R.M.A.: Stokes-vector and Mueller-matrix polarimetry. J. Opt. Soc. Am. A 33(7), 1396–1408 (2016) https://doi.org/10.1364/JOSAA.33.001396 Li et al. [2021] Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, P., Dong, Y., Wan, J., He, H., Aziz, T., Ma, H.: Polaromics: deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen. Journal of Physics D: Applied Physics 55(3), 034002 (2021) https://doi.org/10.1088/1361-6463/ac292f Lu and Chipman [1996] Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Lu, S.-Y., Chipman, R.A.: Interpretation of Mueller matrices based on polar decomposition. J. Opt. Soc. Am. A 13(5), 1106–1113 (1996) https://doi.org/10.1364/JOSAA.13.001106 San José and Gil [2023] San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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[2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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[2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. 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IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  7. San José, I., Gil, J.J.: Extended representation of Mueller matrices. Photonics 10(1) (2023) https://doi.org/10.3390/photonics10010093 Pierangelo et al. [2011] Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. 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Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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[2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. 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  8. Pierangelo, A., Benali, A., Antonelli, M.-R., Novikova, T., Validire, P., Gayet, B., Martino, A.D.: Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging. Opt. Express 19(2), 1582–1593 (2011) https://doi.org/10.1364/OE.19.001582 Rehbinder et al. [2016] Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rehbinder, J., Haddad, H., Deby, S., Teig, B., Nazac, A., Novikova, T., Pierangelo, A., Moreau, F.: Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation. J. Biomed. Opt. 21(7), 071113 (2016) https://doi.org/10.1117/1.JBO.21.7.071113 Axer et al. [2001] Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, H., Axer, M., Krings, T., Keyserlingk, D.G.: Quantitative estimation of 3-d fiber course in gross histological sections of the human brain using polarized light. Journal of Neuroscience Methods 105(2), 121–131 (2001) https://doi.org/10.1016/S0165-0270(00)00349-6 Axer et al. [2011] Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. 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[2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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[2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. 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[2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. 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[2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Axer, M., Graessel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Huetz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Frontiers in Neuroinformatics 5 (2011) https://doi.org/10.3389/fninf.2011.00034 Schucht et al. [2020] Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. 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[2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. 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[2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Schucht, P., Lee, H.R., Mezouar, H.M., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Transactions on Medical Imaging 39(12), 4376–4382 (2020) https://doi.org/10.1109/tmi.2020.3018439 Rodríguez-Núñez et al. [2021] Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. 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[2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. 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[2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? 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[1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. 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[2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. 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Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. 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  13. Rodríguez-Núñez, O., Schucht, P., Lee, H.R., Mezouar, M.H., Hewer, E., Raabe, A., Murek, M., Zubak, I., Goldberg, J., Kövari, E., Pierangelo, A., Novikova, T.: Retardance map of brain white matter: a potential game changer for the intra-operative navigation during brain tumor surgery. In: SPIE Translational Biophotonics: Diagnostics and Therapeutics (2021). https://doi.org/10.1117/12.2614598 McKinley et al. [2022] McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 McKinley, R., Felger, L.A., Hewer, E., Maragkou, T., Murek, M., Novikova, T., Rodríguez-Núñez, O., Pierangelo, A., Schucht, P.: Machine learning for white matter fibre tract visualization in the human brain via mueller matrix polarimetric data. In: Unconventional Optical Imaging III, vol. 12136, pp. 93–98 (2022). https://doi.org/10.1117/12.2624465 . SPIE Novikova et al. [2023] Novikova, T., Pierangelo, A., Schucht, P., Meglinski, I., Rodríguez-Núñez, O., Lee, H.R.: In: Ramella-Roman, J.C., Novikova, T. (eds.) Mueller Polarimetry of Brain Tissues, pp. 205–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-04741-1_8 Ossikovski et al. [2008] Ossikovski, R., Anastasiadou, M., De Martino, A.: Product decompositions of depolarizing Mueller matrices with negative determinants. Optics Comm. 281(9), 2406–2410 (2008) https://doi.org/10.1016/j.optcom.2007.12.076 Ho et al. [2020] Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models, vol. 33, pp. 6840–6851 (2020). https://dl.acm.org/doi/pdf/10.5555/3495724.3496298 Wang [2022] Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Wang, P.: Denoising diffusion probabilistic model in pytorch. Technical report (2022). https://github.com/lucidrains/denoising-diffusion-pytorch Sohl-Dickstein et al. [2015] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: ICML, vol. 37, pp. 2256–2265 (2015). https://proceedings.mlr.press/v37/sohl-dickstein15.html Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. 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[2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  20. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 Moriconi [2022] Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  21. Moriconi, S.: libmpmuelmat - computational tools for MPI. Technical report (2022). https://github.com/stefanomoriconi/libmpMuelMat Yang et al. [2022] Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  22. Yang, X., Zhao, Q., Huang, T., Hu, Z., Bu, T., He, H., Hou, A., Li, M., Xiao, Y., Ma, H.: Deep learning for denoising in a Mueller matrix microscope. Biomed. Opt. Express 13(6), 3535–3551 (2022) https://doi.org/10.1364/BOE.457219 Gibbons and Chakraborti [2014] Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  23. Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference: Revised and Expanded. CRC press, ??? (2014). https://doi.org/10.4324/9780203911563 Li et al. [2020] Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  24. Li, X., Li, H., Lin, Y., Guo, J., Yang, J., Yue, H., Li, K., Li, C., Cheng, Z., Hu, H., Liu, T.: Learning-based denoising for polarimetric images. Opt. Express 28(11), 16309–16321 (2020) https://doi.org/10.1364/OE.391017 Huang et al. [1979] Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  25. Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13–18 (1979) https://doi.org/10.1109/tassp.1979.1163188 Buades et al. [2005] Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  26. Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE CVPR, vol. 2, pp. 60–652 (2005). https://doi.org/10.1109/CVPR.2005.38 Krissian et al. [2007] Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  27. Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 16(5), 1412–1424 (2007) https://doi.org/10.1109/TIP.2007.891803 Rodríguez-Núñez and Novikova [2022] Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015 Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
  28. Rodríguez-Núñez, O., Novikova, T.: Polarimetric techniques for the structural studies and diagnosis of brain. Advanced Optical Technologies 11(5-6), 157–171 (2022) https://doi.org/10.1515/aot-2022-0015
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