[1] Goldstein D H. Polarized Light[M]. Boca Raton: CRC Press, 2017.
[2] Liu X, Zhang L, Zhai X, et al. Polarization lidar: Principles and applications[C]//Photonics. MDPI, 2023, 10(10): 1118.
[3] Li X, Yan L, Qi P, et al. Polarimetric imaging via deep learning: A review [J]. Remote Sensing, 2023, 15(6): 1540. doi:  10.3390/rs15061540
[4] Breugnot S, Clemenceau P. Modeling and performances of a polarization active imager at λ= 806 nm [J]. Optical Engineering, 2000, 39(10): 2681-2688. doi:  10.1117/1.1286140
[5] Marino A, Dierking W, Wesche C. A depolarization ratio anomaly detector to identify icebergs in sea ice using dual-polarization SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9): 5602-5615. doi:  10.1109/TGRS.2016.2569450
[6] Dong Y, Wan J, Wang X, et al. A polarization-imaging-based machine learning framework for quantitative pathological diagnosis of cervical precancerous lesions [J]. IEEE Transactions on Medical Imaging, 2021, 40(12): 3728-3738. doi:  10.1109/TMI.2021.3097200
[7] Ramella-Roman J C, Novikova T. Polarized Light in Biomedical Imaging and Sensing: Clinical and Preclinical Applications[M]. Berlin: Springer, 2022.
[8] Novikova T, Ramella-Roman J C. Is a complete Mueller matrix necessary in biomedical imaging? [J]. Optics Letters, 2022, 47(21): 5549-5552. doi:  10.1364/OL.471239
[9] Sun M, He H, Zeng N, et al. Characterizing the microstructures of biological tissues using Mueller matrix and transformed polarization parameters [J]. Biomedical Optics Express, 2014, 5(12): 4223-4234. doi:  10.1364/BOE.5.004223
[10] Liu F, Wei Y, Han P, et al. Polarization-based exploration for clear underwater vision in natural illumination [J]. Optics Express, 2019, 27(3): 3629-3641. doi:  10.1364/OE.27.003629
[11] Li X, Hu H, Zhao L, et al. Polarimetric image recovery method combining histogram stretching for underwater imaging [J]. Scientific Reports, 2018, 8(1): 12430. doi:  10.1038/s41598-018-30566-8
[12] Hu H, Qi P, Li X, et al. Underwater imaging enhancement based on a polarization filter and histogram attenuation prior [J]. Journal of Physics D: Applied Physics, 2021, 54(17): 175102. doi:  10.1088/1361-6463/abdc93
[13] He C, He H, Chang J, et al. biomedical and clinical applications: a review [J]. Light: Science & Applications, 2021, 10(1): 194.
[14] Zuo Chao, Chen Qian. Computational optical imaging: An overview [J]. Infrared and Laser Engineering, 2022, 51(2): 20220110. (in Chinese) doi:  10.3788/IRLA20220110
[15] Han P, Liu F, Wei Y, et al. Optical correlation assists to enhance underwater polarization imaging performance [J]. Optics and Lasers in Engineering, 2020, 134: 106256. doi:  10.1016/j.optlaseng.2020.106256
[16] Li X, Xu J, Zhang L, et al. Underwater image restoration via Stokes decomposition [J]. Optics Letters, 2022, 47(11): 2854-2857. doi:  10.1364/OL.457964
[17] Shen X, Carnicer A, Javidi B. Three-dimensional polarimetric integral imaging under low illumination conditions [J]. Optics Letters, 2019, 44(13): 3230-3233. doi:  10.1364/OL.44.003230
[18] Schechner Y Y, Narasimhan S G, Nayar S K. Polarization-based vision through haze [J]. Applied Optics, 2003, 42(3): 511-525. doi:  10.1364/AO.42.000511
[19] Schechner Y Y, Karpel N. Recovery of underwater visibility and structure by polarization analysis [J]. IEEE Journal of oceanic engineering, 2005, 30(3): 570-587. doi:  10.1109/JOE.2005.850871
[20] Faisan S, Heinrich C, Rousseau F, et al. Joint filtering estimation of Stokes vector images based on a nonlocal means approach [J]. JOSA A, 2012, 29(9): 2028-2037. doi:  10.1364/JOSAA.29.002028
[21] Faisan S, Heinrich C, Sfikas G, et al. Estimation of Mueller matrices using non-local means filtering [J]. Optics Express, 2013, 21(4): 4424-4438. doi:  10.1364/OE.21.004424
[22] Zuo C, Qian J, Feng S, et al. Deep learning in optical metrology: a review [J]. Light: Science & Applications, 2022, 11(1): 39.
[23] Barbastathis G, Ozcan A, Situ G. On the use of deep learning for computational imaging [J]. Optica, 2019, 6(8): 921-943. doi:  10.1364/OPTICA.6.000921
[24] Luo Haibo, Zhang Junchao, Gai Xingqin, et al. Development status and prospects of polarization imaging technology ( Invited) [J]. Infrared and Laser Engineering, 2022, 51(1): 20210987. (in Chinese)
[25] Liu Fei, Sun Shaojie, Han Pingli, et al. Clear underwater vision in non-uniform scattering field by low-rank-and-sparse-decomposition-based polarization imaging [J]. Acta Phys Sin, 2021, 70(16): 164201. (in Chinese) doi:  10.7498/aps.70.20210314
[26] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444. doi:  10.1038/nature14539
[27] Chai J, Zeng H, Li A, et al. Deep learning in computer vision: A critical review of emerging techniques and application scenarios [J]. Machine Learning with Applications, 2021, 6: 100134. doi:  10.1016/j.mlwa.2021.100134
[28] Lauriola I, Lavelli A, Aiolli F. An introduction to deep learning in natural language processing: Models, techniques, and tools [J]. Neurocomputing, 2022, 470: 443-456. doi:  10.1016/j.neucom.2021.05.103
[29] Li X, Han Y, Wang H, et al. Polarimetric imaging through scattering media: A review [J]. Frontiers in Physics, 2022, 10: 815296. doi:  10.3389/fphy.2022.815296
[30] Li Zhiyuan, Zhai Aiping, Ji Yingze, et al. Research, application and progress of optical polarization imaging technology [J]. Infrared and Laser Engineering, 2023, 52(9): 20220808. (in Chinese) doi:  10.3788/IRLA20220808
[31] Kliger D S, Lewis J W. Polarized Light in Optics and Spectroscopy[M]. Amsterdam: Elsevier, 2012.
[32] Wang Xia, Zhang Mingyang, Chen Zhenyue, et al. Overview on system structure of active polarization imaging [J]. Infrared and Laser Engineering, 2013, 42(8): 2244-2251. (in Chinese)
[33] Lu S Y, Chipman R A. Interpretation of Mueller matrices based on polar decomposition [J]. JOSA A, 1996, 13(5): 1106-1113. doi:  10.1364/JOSAA.13.001106
[34] Sheng S, Chen X, Chen C, et al. Eigenvalue calibration method for dual rotating-compensator Mueller matrix polarimetry [J]. Optics Letters, 2021, 46(18): 4618-4621. doi:  10.1364/OL.437542
[35] Smith M H. Optimization of a dual-rotating-retarder Mueller matrix polarimeter [J]. Applied Optics, 2002, 41(13): 2488-2493. doi:  10.1364/AO.41.002488
[36] Liu F, Han P, Wei Y, et al. Deeply seeing through highly turbid water by active polarization imaging [J]. Optics Letters, 2018, 43(20): 4903-4906. doi:  10.1364/OL.43.004903
[37] Hu H, Huang Y, Li X, et al. UCRNet: Underwater color image restoration via a polarization-guided convolutional neural network [J]. Frontiers in Marine Science, 2022, 9: 1031549. doi:  10.3389/fmars.2022.1031549
[38] Li Z, Jiang H, Cao M, et al. Polarized color image denoising [C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023: 9873-9882.
[39] Xu X, Wan M, Ge J, et al. ColorPolarNet: Residual dense network-based chromatic intensity-polarization imaging in low-light environment [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-10.
[40] Ding X, Wang Y, Fu X. Multi-polarization fusion generative adversarial networks for clear underwater imaging [J]. Optics and Lasers in Engineering, 2022, 152: 106971. doi:  10.1016/j.optlaseng.2022.106971
[41] Lin B, Fan X, Guo Z. Self-attention module in a multi-scale improved U-net (SAM-MIU-net) motivating high-performance polarization scattering imaging [J]. Optics Express, 2023, 31(2): 3046-3058. doi:  10.1364/OE.479636
[42] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 770-778.
[43] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 4700-4708.
[44] Song Junhong, Xiao Zuojiang, Li Yingchao, et al. Influence of concentration variation of oil mist particles on scattering mueller matrix [J]. Acta Optica Sinica, 2021, 41(23): 2301001. (in Chinese)
[45] Su Lewei, Duan Cunli, Sun Liang et al. Influence of optical polarization on underwater range-gated imaging for target recognition distance under different water quality conditions [J]. Infrared and Laser Engineering, 2024, 53(1): 20230372. (in Chinese)
[46] Ramella-Roman J C, Prahl S A, Jacques S L. Three Monte Carlo programs of polarized light transport into scattering media: part I [J]. Optics Express, 2005, 13(12): 4420-4438. doi:  10.1364/OPEX.13.004420
[47] Wang X, Hu T, Li D, et al. Performances of polarization-retrieve imaging in stratified dispersion media [J]. Remote Sensing, 2020, 12(18): 2895. doi:  10.3390/rs12182895
[48] Chen C, Chen Q, Xu J, et al. Learning to see in the dark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3291-3300.
[49] Zhou C, Teng M, Han Y, et al. Learning to dehaze with polarization [J]. Advances in Neural Information Processing Systems, 2021, 34: 11487-11500.
[50] Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks [J]. IEEE Transactions on Computational Imaging, 2016, 3(1): 47-57.
[51] Guo Enlai, Shi Yingjie, Zhu Shuo, et al. Scattering imaging with deep learning: Physical and data joint modeling optimization ( invited) [J]. Infrared and Laser Engineering, 2022, 51(8): 20220563. (in Chinese) doi:  10.3788/IRLA20220563
[52] Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution [C]//Computer Vision–ECCV 2016: 14th European Conference, 2016: 694-711.
[53] Hu H, Zhang Y, Li X, et al. Polarimetric underwater image recovery via deep learning [J]. Optics and Lasers in Engineering, 2020, 133: 106152. doi:  10.1016/j.optlaseng.2020.106152
[54] Agaian S S, Panetta K, Grigoryan A M. A new measure of image enhancement [C]//IASTED International Conference on Signal Processing & Communication, 2000: 19-22.
[55] Xiang Y, Yang X, Ren Q, et al. Underwater polarization imaging recovery based on polarimetric residual dense network [J]. IEEE Photonics Journal, 2022, 14(6): 1-6.
[56] Yang K, Han P, Gong R, et al. High-quality 3D shape recovery from scattering scenario via deep polarization neural networks [J]. Optics and Lasers in Engineering, 2024, 173: 107934. doi:  10.1016/j.optlaseng.2023.107934
[57] Li D, Lin B, Wang X, et al. High-performance polarization remote sensing with the modified U-net based deep-learning network [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-10.
[58] Almahairi A, Rajeshwar S, Sordoni A, et al. Augmented cyclegan: Learning many-to-many mappings from unpaired data [C]//International Conference on Machine Learning, PMLR, 2018: 195-204.
[59] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2223-2232.
[60] Qi P, Li X, Han Y, et al. U2R-pGAN: Unpaired underwater-image recovery with polarimetric generative adversarial network [J]. Optics and Lasers in Engineering, 2022, 157(10): 107112. doi:  10.1016/j.optlaseng.2022.107112
[61] Zoph B, Ghiasi G, Lin T Y, et al. Rethinking pre-training and self-training [J]. Advances in Neural Information Processing Systems, 2020, 33: 3833-3845.
[62] Shi Y, Guo E, Bai L, et al. Polarization-based haze removal using self-supervised network [J]. Frontiers in Physics, 2022, 9: 789232. doi:  10.3389/fphy.2021.789232
[63] Zhu Y, Zeng T, Liu K, et al. Full scene underwater imaging with polarization and an untrained network [J]. Optics Express, 2021, 29(25): 41865-41881. doi:  10.1364/OE.444755
[64] Liang Jian, Ju Haijuan, Zhang Wenfei, et al. Review of optical polarimetric dehazing technique [J]. Acta Optica Sinica, 2017, 37(4): 0400001. (in Chinese) doi:  10.3788/AOS201737.0400001
[65] Liang J, Ren L, Qu E, et al. Method for enhancing visibility of hazy images based on polarimetric imaging [J]. Photonics Research, 2014, 2(1): 38-44. doi:  10.1364/PRJ.2.000038
[66] Hu H, Han Y, Li X, et al. Physics-informed neural network for polarimetric underwater imaging [J]. Optics Express, 2022, 30(13): 22512-22522. doi:  10.1364/OE.461074
[67] Lin B, Fan X, Peng P, et al. Dynamic polarization fusion network (DPFN) for imaging in different scattering systems [J]. Optics Express, 2024, 32(1): 511-525. doi:  10.1364/OE.507711
[68] Li S, Ye W, Liang H, et al. K-SVD based denoising algorithm for DoFP polarization image sensors [C]//2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018: 1-5.
[69] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005: 60-65.
[70] Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095. doi:  10.1109/TIP.2007.901238
[71] Li X, Li H, Lin Y, et al. Learning-based denoising for polarimetric images [J]. Optics Express, 2020, 28(11): 16309-16321. doi:  10.1364/OE.391017
[72] Usmani K, O’Connor T, Javidi B. Three-dimensional polarimetric image restoration in low light with deep residual learning and integral imaging [J]. Optics Express, 2021, 29(18): 29505-29517. doi:  10.1364/OE.435900
[73] Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising [J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155. doi:  10.1109/TIP.2017.2662206
[74] Hu H, Lin Y, Li X, et al. IPLNet: a neural network for intensity-polarization imaging in low light [J]. Optics Letters, 2020, 45(22): 6162-6165. doi:  10.1364/OL.409673
[75] Yosinski J, Clune J, Bengio Y, et al. Advances in neural information processing systems [C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 3320–3328.
[76] Hu H, Jin H, Liu H, et al. Polarimetric image denoising on small datasets using deep transfer learning [J]. Optics & Laser Technology, 2023, 166: 109632.
[77] Hu Haofeng, Jin Huifeng, Li Xiaobo, et al. Polarization image denoising based on unsupervised learning [J]. Acta Optica Sinica, 2023, 43(4): 0410001. (in Chinese)
[78] Lehtinen J, Munkberg J, Hasselgren J, et al. Noise2Noise: Learning image restoration without clean data [DB/OL]. (2018-03-12) [2024-02-29]. https://arxiv.org/abs/1803.04189.
[79] Liu H, Li X, Cheng Z, et al. Pol2Pol: self-supervised polarimetric image denoising [J]. Optics Letters, 2023, 48(18): 4821-4824. doi:  10.1364/OL.500198
[80] Liu H, Zhang Y, Cheng Z, et al. Attention-based neural network for polarimetric image denoising [J]. Optics Letters, 2022, 47(11): 2726-2729. doi:  10.1364/OL.458514
[81] Liu H, Li X, Cheng Z, et al. Polarization maintaining 3-D convolutional neural network for color polarimetric images denoising [J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-9.