[1] Han J F, Yang K C, Xia M et al. Resolution enhancement in active underwater polarization imaging with modulation transfer function analysis[J]. Applied Optics, 54, 3294-3302(2015).

[2] Huang B J, Liu T G, Hu H F et al. Underwater image recovery considering polarization effects of objects[J]. Optics Express, 24, 9826-9838(2016).

[3] Xu Q, Guo Z Y, Tao Q Q et al. Multi-spectral characteristics of polarization retrieve in various atmospheric conditions[J]. Optics Communications, 339, 167-170(2015).

[4] Hu T W, Shen F, Wang K P et al. Broad-band transmission characteristics of polarizations in foggy environments[J]. Atmosphere, 10, 342(2019).

[5] Horstmeyer R, Ruan H W, Yang C. Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue[J]. Nature Photonics, 9, 563-571(2015).

[6] Li D K, Xu C X, Zhang M et al. Measuring glucose concentration in a solution based on the indices of polarimetric purity[J]. Biomedical Optics Express, 12, 2447-2459(2021).

[7] Shen F, Zhang M, Guo K et al. The depolarization performances of scattering systems based on the Indices of Polarimetric Purity (IPPs)[J]. Optics Express, 27, 28337-28349(2019).

[8] Ntziachristos V. Going deeper than microscopy: the optical imaging frontier in biology[J]. Nature Methods, 7, 603-614(2010).

[9] Dai Q H, Suo J L, Ji X Y et al[M]. Computational photography: computational capture of plenoptic visual information(2016).

[10] Ding J Y, Peng S Y. Photothermal microimaging: a non-invasive and high-resolution imaging technique[J]. Laser & Optoelectronics Progress, 60, 2200001(2023).

[11] Zhang Z Y, Shi Y, Dai S X et al. Research progresses on non-line-of-sight imaging technology[J]. Laser & Optoelectronics Progress, 60, 1400001(2023).

[12] Jin X, Wang X Y, Du D Y et al. Progress and prospect of scattering imaging[J]. Laser & Optoelectronics Progress, 58, 1811002(2021).

[13] Osnabrugge G, Horstmeyer R, Papadopoulos I N et al. Generalized optical memory effect[J]. Optica, 4, 886-892(2017).

[14] Haskel M, Stern A. Modeling optical memory effects with phase screens[J]. Optics Express, 26, 29231-29243(2018).

[15] Yoon J, Lee K, Park J et al. Measuring optical transmission matrices by wavefront shaping[J]. Optics Express, 23, 10158-10167(2015).

[16] Drémeau A, Liutkus A, Martina D et al. Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques[J]. Optics Express, 23, 11898-11911(2015).

[17] Paudel H P, Stockbridge C, Mertz J et al. Focusing polychromatic light through strongly scattering media[J]. Optics Express, 21, 17299-17308(2013).

[18] Osnabrugge G, Amitonova L V, Vellekoop I M. Blind focusing through strongly scattering media using wavefront shaping with nonlinear feedback[J]. Optics Express, 27, 11673-11688(2019).

[19] Li D K, Xu C X, Yan L S et al. High-performance scanning-mode polarization based computational ghost imaging (SPCGI)[J]. Optics Express, 30, 17909-17921(2022).

[20] Ferri F, Magatti D, Lugiato L A et al. Differential ghost imaging[J]. Physical Review Letters, 104, 253603(2010).

[21] Xu G Q, Zhang X Z, Wang W et al. Dynamic manipulation of mid-infrared polarized thermal emission enabled by In3SbTe2 metasurfaces[J]. Advanced Photonics Nexus, 4, 016008(2025).

[22] Li D K, Montes I, Canabal-Carbia M et al. Enhanced characterization of depolarizing samples using indices of polarization purity and polarizance-reflection-transformation spaces[J]. Advanced Photonics Nexus, 4, 016009(2025).

[23] Ding H J, Ding M L, Lv T Y et al. Polarimetric observable based optical remote sensing systems for heterogeneous layered scattering environments[J]. Optics and Lasers in Engineering, 189, 108916(2025).

[24] Li S, Deng M, Lee J et al. Imaging through glass diffusers using densely connected convolutional networks[J]. Optica, 5, 803-813(2018).

[25] Li Y Z, Xue Y J, Tian L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media[J]. Optica, 5, 1181-1190(2018).

[26] Lin B, Fan X Q, Li D K et al. Research progress of imaging through scattering media based on deep learning[J]. Chinese Journal of Quantum Electronics, 39, 880-898(2022).

[27] Zhu S, Guo E L, Gu J et al. Efficient color imaging through unknown opaque scattering layers via physics-aware learning[J]. Optics Express, 29, 40024-40037(2021).

[28] Lyu M, Wang H, Li G W et al. Learning-based lensless imaging through optically thick scattering media[J]. Advanced Photonics, 1, 036002(2019).

[29] Yang M, Liu Z H, Cheng Z D et al. Deep hybrid scattering image learning[J]. Journal of Physics D: Applied Physics, 52, 115105(2019).

[30] Li D K, Lin B, Wang X Y et al. High-performance polarization remote sensing with the modified U-Net based deep-learning network[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5621110(2022).

[31] Lin B, Fan X Q, Guo Z Y. Self-attention module in a multi-scale improved U-Net (SAM-MIU-Net) motivating high-performance polarization scattering imaging[J]. Optics Express, 31, 3046-3058(2023).

[32] Ma Q, Jiang J J, Liu X M et al. Deep unfolding network for spatiospectral image super-resolution[J]. IEEE Transactions on Computational Imaging, 8, 28-40(2021).

[33] Liu Z C, Roy M, Prasad D K et al. Physics-guided loss functions improve deep learning performance in inverse scattering[J]. IEEE Transactions on Computational Imaging, 8, 236-245(2022).

[34] Lin B, Chen W Y, Fan X Q et al. Transformer-based improved U-Net for high-performance underwater polarization imaging[J]. Optics & Laser Technology, 181, 111664(2025).

[35] Fan X Q, Lin B, Guo Z Y. Infrared polarization-empowered full-time road detection via lightweight multi-pathway collaborative 2D/3D convolutional networks[J]. IEEE Transactions on Intelligent Transportation Systems, 25, 12762-12775(2024).

[36] Fan X Q, Ding M L, Lv T Y et al. Meta-DNET-UPI: efficient underwater polarization imaging combining deformable convolutional networks and meta-learning[J]. Optics & Laser Technology, 187, 112900(2025).

[37] Luan X, Wang Q, Fan H J et al. Feature distillation and guide network for unsupervised underwater image enhancement[J]. Engineering Applications of Artificial Intelligence, 136, 109006(2024).

[38] Zhu P L, Liu Y C, Wen Y Q et al. Unsupervised underwater image enhancement via content-style representation disentanglement[J]. Engineering Applications of Artificial Intelligence, 126, 106866(2023).

[39] Xiao L Z, Wang S B, Zhang J et al. Poisson2Poisson-Sparse: unsupervised poisson noise image denoising based on sparse modeling[J]. Signal Processing, 230, 109870(2025).

[40] Ni Z K, Yang W H, Wang S Q et al. Towards unsupervised deep image enhancement with generative adversarial network[J]. IEEE Transactions on Image Processing, 29, 9140-9151(2020).

[41] Jiang Q P, Mao Y D, Cong R M et al. Unsupervised decomposition and correction network for low-light image enhancement[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 19440-19455(2022).

[42] Zhou W H, Zhu D M, Shi M et al. Deep images enhancement for turbid underwater images based on unsupervised learning[J]. Computers and Electronics in Agriculture, 202, 107372(2022).

[43] Hu J J, Guo X Y, Chen J F et al. A two-stage unsupervised approach for low light image enhancement[J]. IEEE Robotics and Automation Letters, 6, 8363-8370(2021).

[44] Lin F, Zhang H Q, Wang J et al. Unsupervised image enhancement under non-uniform illumination based on paired CNNs[J]. Neural Networks, 170, 202-214(2024).

[45] Fu Y, Hong Y, Chen L W et al. LE-GAN: unsupervised low-light image enhancement network using attention module and identity invariant loss[J]. Knowledge-Based Systems, 240, 108010(2022).

[46] Li J, Zhang C Y, Zhu W et al. A comprehensive survey of image generation models based on deep learning[J]. Annals of Data Science, 12, 141-170(2025).

[47] Cao H Q, Tan C, Gao Z Y et al. A survey on generative diffusion models[J]. IEEE Transactions on Knowledge and Data Engineering, 36, 2814-2830(2024).

[48] Chen H, Xiang Q, Hu J X et al. Comprehensive exploration of diffusion models in image generation: a survey[J]. Artificial Intelligence Review, 58, 99(2025).

[49] de Souza V L T, Marques B A D, Batagelo H C et al. A review on generative adversarial networks for image generation[J]. Computers & Graphics, 114, 13-25(2023).

[50] Ohno H. Auto-encoder-based generative models for data augmentation on regression problems[J]. Soft Computing, 24, 7999-8009(2020).

[51] Sonawane J, Patil M, Birajdar G. A novel feature extraction and mapping using convolutional autoencoder for enhancement of Underwater image/video[J]. ITM Web of Conferences, 44, 03066(2022).

[52] Yu Y, Qin C F. An end-to-end underwater-image-enhancement framework based on fractional integral retinex and unsupervised autoencoder[J]. Fractal and Fractional, 7, 70(2023).

[53] Saleh A, Sheaves M, Jerry D et al. Adaptive deep learning framework for robust unsupervised underwater image enhancement[J]. Expert Systems with Applications, 268, 126314(2025).

[54] Thai D H, Fei X Q, Le M T et al. Riesz-quincunx-UNet variational autoencoder for unsupervised satellite image denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5404519(2023).

[55] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C], 2672-2680(2014).

[56] Li J, Skinner K A, Eustice R M et al. WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 3, 387-394(2018).

[57] She C Y, Chen T, Duan S K et al. SAGAN: deep semantic-aware generative adversarial network for unsupervised image enhancement[J]. Knowledge-Based Systems, 281, 111053(2023).

[58] Yin J K, Wang Y, Guan B W et al. Unsupervised underwater image enhancement based on disentangled representations via double-order contrastive loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 62, 4201815(2024).

[59] Shi Y M, Wang B Q, Wu X P et al. Unsupervised low-light image enhancement by extracting structural similarity and color consistency[J]. IEEE Signal Processing Letters, 29, 997-1001(2022).

[60] Li J F, Li Y P, Zhuo L et al. USID-net: unsupervised single image dehazing network via disentangled representations[J]. IEEE Transactions on Multimedia, 25, 3587-3601(2022).

[61] Liu J L, Wang S L, Chen C et al. DFP-Net: an unsupervised dual-branch frequency-domain processing framework for single image dehazing[J]. Engineering Applications of Artificial Intelligence, 136, 109012(2024).

[62] Yang S L, Qu B W, Liu G S et al. Unsupervised learning polarimetric underwater image recovery under nonuniform optical fields[J]. Applied Optics, 60, 8198-8205(2021).

[63] Yu W S, Zhao L Q, Zhong T. Unsupervised low-light image enhancement based on generative adversarial network[J]. Entropy, 25, 932(2023).

[64] Wang R J, Jiang B, Yang C et al. MAGAN: unsupervised low-light image enhancement guided by mixed-attention[J]. Big Data Mining and Analytics, 5, 110-119(2022).

[65] Zhou H P, Zeng X B, Lin B et al. Polarization motivating high-performance weak targets’ imaging based on a dual-discriminator GAN[J]. Optics Express, 32, 3835-2851(2024).

[66] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2242-2251(2017).

[67] Yamazaki K, Horisaki R, Tanida J. Imaging through scattering media based on semi-supervised learning[J]. Applied Optics, 59, 9850-9854(2020).

[68] Engin D, Genc A, Ekenel H K. Cycle-dehaze: enhanced CycleGAN for single image dehazing[C], 938-9388(2018).

[69] Wang Y Z, Yan X F, Guan D H et al. Cycle-SNSPGAN: towards real-world image dehazing via cycle spectral normalized soft likelihood estimation patch GAN[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 20368-20382(2022).

[70] Sun H, Luo Z M, Ren D et al. Unsupervised multi-branch network with high-frequency enhancement for image dehazing[J]. Pattern Recognition, 156, 110763(2024).

[71] Shi Y J, Guo E L, Bai L F et al. Prior-free imaging unknown target through unknown scattering medium[J]. Optics Express, 30, 17635-17651(2022).

[72] Chen X, Li Y F, Kong C H et al. Unpaired image dehazing with physical-guided restoration and depth-guided refinement[J]. IEEE Signal Processing Letters, 29, 587-591(2022).

[73] Jiang Y F, Gong X Y, Liu D et al. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 30, 2340-2349(2021).

[74] Wang Y Z, Yan X F, Wang F et al. UCL-dehaze: toward real-world image dehazing via unsupervised contrastive learning[J]. IEEE Transactions on Image Processing, 33, 1361-1374(2024).

[75] Liang D, Chu J L, Cui Y G et al. NPT-UL: an underwater image enhancement framework based on nonphysical transformation and unsupervised learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 62, 5608819(2024).

[76] Song Y, Ermon S. Improved techniques for training score-based generative models[EB/OL]. https://arxiv.org/abs/2006.09011v2

[77] Song Y, Ermon S. Generative modeling by estimating gradients of the data distribution[EB/OL]. https://arxiv.org/abs/1907.05600

[78] Ho J, Jain A N, Abbeel P. Denoising diffusion probabilistic models[EB/OL]. https://arxiv.org/abs/2006.11239

[79] Sivaanpu A, Noga M, Becher H et al. Denoising echocardiography with an improved diffusion model[C], 1-4(2024).

[80] Xu M Z, Ma J, Zhu Y Y. Dual-diffusion: dual conditional denoising diffusion probabilistic models for blind super-resolution reconstruction in RSIs[J]. IEEE Geoscience and Remote Sensing Letters, 20, 6008505(2023).

[81] Li K Y, Qi M B, Liu Y M et al. Fusing infrared polarization images for road detection via denoising diffusion probabilistic models[J]. Optics Letters, 49, 5312-5315(2024).

[82] Palanirajan S, Natarajan B, Baalaji A et al. Revamped lane detection in hazy environments using denoising diffusion probabilistic model[C], 546-551(2024).

[83] Li H, Wu X J, Kittler J. Infrared and visible image fusion using a deep learning framework[C], 2705-2710(2018).

[84] Yang A P, Liu Y M, Wang J B et al. Visual-quality-driven unsupervised image dehazing[J]. Neural Networks, 167, 1-9(2023).

[85] Ding B S, Zhang R H, Xu L X et al. U2D2Net: unsupervised unified image dehazing and denoising network for single hazy image enhancement[J]. IEEE Transactions on Multimedia, 26, 202-217(2023).

[86] Cheng H Y, Zhang D Q, Zhu J C et al. Underwater target detection utilizing polarization image fusion algorithm based on unsupervised learning and attention mechanism[J]. Sensors, 23, 5594(2023).

[87] Lee H S, Sohn K, Min D B. Unsupervised low-light image enhancement using bright channel prior[J]. IEEE Signal Processing Letters, 27, 251-255(2020).

[88] Wang Y T, Zhuo S J, Tao D P et al. Automatic local exposure correction using bright channel prior for under-exposed images[J]. Signal Processing, 93, 3227-3238(2013).

[89] Nguyen H, Tran D, Nguyen K et al. PSENet: progressive self-enhancement network for unsupervised extreme-light image enhancement[C], 1756-1765(2023).

[90] Liu Y K, Wan B W, Shi D M et al. Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution[J]. Remote Sensing, 15, 364(2023).

[91] Chang X Y, Zhao R F, Jiang S W et al. Complex-domain-enhancing neural network for large-scale coherent imaging[J]. Advanced Photonics Nexus, 2, 046006(2023).

[92] Yang J G, Choi S, Kim J et al. Recent advances in deep-learning-enhanced photoacoustic imaging[J]. Advanced Photonics Nexus, 2, 054001(2023).

[93] Wang J Y, Fan J C, Zhou B et al. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2, 016012(2023).