Revolutionizing optical imaging: computational imaging via deep learning

[66] X. Ma et al. Research on single-frame super-resolution reconstruction algorithm for low resolution cell images based on convolutional neural network, 369(2018).

[67] C. Dong et al. Accelerating the super-resolution convolutional neural network, 391(2016).

[68] H. Zhang et al. Super-resolution generative adversarial network (SRGAN) enabled on-chip contact microscopy. J. Phys. D, 54, 394005(2021). https://doi.org/10.1088/1361-6463/ac1138

[69] Y. Fang et al. Classification of white blood cells by convolution neural network in lens-free imaging system, 1(2018).

[70] S. Li et al. A deep learning feature fusion algorithm based on Lensless cell detection system, 1(2020).

[71] M. Baik et al. Label-Free CD34+ cell identification using deep learning and lens-free shadow imaging technology. Biosensors, 13, 993(2023). https://doi.org/10.3390/bios13120993

[72] R. Vaghashiya et al. Machine learning based lens-free shadow imaging technique for field-portable cytometry. Biosensors, 12, 144(2022). https://doi.org/10.3390/bios12030144

[73] S. J. Moon et al. Integrating microfluidics and lensless imaging for point-of-care testing. Biosens. Bioelectron., 24, 3208(2009). https://doi.org/10.1016/j.bios.2009.03.037

[74] Y. Wu et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704(2018). https://doi.org/10.1364/OPTICA.5.000704

[75] K. Wang et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. Opt. Lett., 44, 4765(2019). https://doi.org/10.1364/OL.44.004765

[76] K. Wang et al. Y4-Net: a deep learning solution to one-shot dual-wavelength digital holographic reconstruction. Opt. Lett., 45, 4220(2020). https://doi.org/10.1364/OL.395445

[77] Z. Ren et al. End-to-end deep learning framework for digital holographic reconstruction. Adv. Photonics, 1, 016004(2019). https://doi.org/10.1117/1.AP.1.1.016004

[78] T. Zeng et al. Redcap: residual encoder-decoder capsule network for holographic image reconstruction. Opt. Express, 28, 4876(2020). https://doi.org/10.1364/OE.383350

[79] Z. Luo et al. Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks. Opt. Express, 27, 13581(2019). https://doi.org/10.1364/OE.27.013581

[80] C. Zhang et al. Lens-free imaging method based on generative adversarial networks. Acta Opt. Sin., 40, 1611003(2020). https://doi.org/10.3788/AOS202040.1611003

[81] L. Chen et al. Image enhancement in lensless inline holographic microscope by inter-modality learning with denoising convolutional neural network. Opt. Commun., 484, 126682(2021). https://doi.org/10.1016/j.optcom.2020.126682

[82] Z. Göröcs et al. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples. Light Sci. Appl., 7, 66(2018). https://doi.org/10.1038/s41377-018-0067-0

[83] Y. Rivenson et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl., 8, 23(2019). https://doi.org/10.1038/s41377-019-0129-y

[84] J. D. Rego et al. Robustness lens image reconstruction via PSF estimation, 403(2021).

[85] K. Monakhova et al. Learned reconstructions for practical mask-based lensless imaging. Opt. Express, 27, 28075(2019). https://doi.org/10.1364/OE.27.028075

[86] J. Wu et al. Real-time, deep-learning aided lensless microscope. Bio. Opt. Express, 14, 4037(2023). https://doi.org/10.1364/BOE.490199

[87] M. S. Asif et al. Flatcam: replacing lenses with masks and computation, 663(2015).

[88] S. S. Khan et al. Flatnet: towards photorealistic scene reconstruction from lensless measurements. IEEE Trans. Pattern Anal. Mach. Intell., 44, 1934(2020). https://doi.org/10.1109/TPAMI.2020.3033882

[89] H. Zhou et al. Lensless cameras using a mask based on almost perfect sequence through deep learning. Opt. Express, 28, 30248(2020). https://doi.org/10.1364/OE.400486

[90] K. Tajima et al. Lensless light-field imaging with multi-phased fresnel zone aperture, 1(2017).

[91] T. Shimano et al. Lensless light-field imaging with Fresnel zone aperture: quasi-coherent coding. Appl. Opt., 57, 2841(2018). https://doi.org/10.1364/AO.57.002841

[92] J. Wu et al. DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging. Opt. Lett., 46, 130(2021). https://doi.org/10.1364/OL.411228

[93] T. Zeng et al. Robust reconstruction with deep learning to handle model mismatch in lensless imaging. IEEE Trans. Comput. Imaging, 7, 1080(2021). https://doi.org/10.1109/TCI.2021.3114542

[94] X. Pan et al. Image reconstruction with transformer for mask-based lensless imaging. Opt. Lett., 47, 1843(2022). https://doi.org/10.1364/OL.455378

[95] D. N. Klyshk. A simple method of preparing pure states of an optical field, of implementing the Einstein–Podolsky–Rosen experiment, and of demonstrating the complementarity principle. Soviet Physics Uspekhi, 31, 74(1988). https://doi.org/10.1070/PU1988v031n01ABEH002537

[96] T. B. Pittman et al. Optical imaging by means of two-photon quantum entanglement. Phys. Rev. A, 52, R3429(1995). https://doi.org/10.1103/PhysRevA.52.R3429

[97] M. Aβmann, M. Bayer. Compressive adaptive computational ghost imaging. Sci. Rep., 3, 1545(2013). https://doi.org/10.1038/srep01545

[98] S. Nakajima et al. Terahertz imaging diagnostics of cancer tissues with a chemometrics technique. Appl. Phy. Lett., 90, 4(2007). https://doi.org/10.1063/1.2433035

[99] P. Clemente et al. Optical encryption based on computational ghost imaging. Opt. Lett., 35, 2391(2010). https://doi.org/10.1364/OL.35.002391

[100] B. I. Erkmen. Computational ghost imaging for remote sensing. JOSA A, 29, 782(2012). https://doi.org/10.1364/JOSAA.29.000782

[101] Y. Mizutani et al. Ghost imaging with deep learning for position map of weakly scattered light source. Nanomanuf. Metrol., 4, 37(2021). https://doi.org/10.1007/s41871-020-00085-0

[102] C. Zhou et al. Real-time physical compression computational ghost imaging based on array spatial light field modulation and deep learning. Opt. Laser. Eng., 156, 107101(2022). https://doi.org/10.1016/j.optlaseng.2022.107101

[103] F. Li et al. Compressive ghost imaging through scattering media with deep learning. Opt. Express, 28, 17395(2020). https://doi.org/10.1364/OE.394639

[104] L. Zhang et al. Research on photon-level ghost imaging restoration based on deep learning. Opt. Commun., 504, 127479(2022). https://doi.org/10.1016/j.optcom.2021.127479

[105] H. Wu et al. Deep-learning denoising computational ghost imaging. Opt. Laser. Eng., 134, 106183(2020). https://doi.org/10.1016/j.optlaseng.2020.106183

[106] H. Song et al. 0.8% Nyquist computational ghost imaging via non-experimental deep learning. Opt. Commun., 520, 128450(2022). https://doi.org/10.1016/j.optcom.2022.128450

[107] Y. Zhu et al. Deep-learning based multi-scale computational ghost imaging for high-performance complex image recovery. Opt. Commun., 554, 129916(2024). https://doi.org/10.1016/j.optcom.2023.129916

[108] C. Zhao et al. Ghost imaging lidar via sparsity constraints. Appl. Phy. Lett., 101, 14(2012). https://doi.org/10.1063/1.4757874

[109] D. Li et al. Multi-mode microscopic imaging technique based on single-pixel imaging principle. Acta Opt. Sin., 43, 2111003(2023). https://doi.org/10.3788/AOS231001

[110] I. Hoshi et al. Single-pixel imaging using a recurrent neural network combined with convolutional layers. Opt. Express, 28, 34069(2020). https://doi.org/10.1364/OE.410191

[111] R. Shang et al. Deep-learning-driven reliable single-pixel imaging with uncertainty approximation(2021).

[112] F. Wang et al. Single-pixel imaging using physics enhanced deep learning. Photon. Res., 10, 104(2021). https://doi.org/10.1364/PRJ.440123

[113] S. Rizvi et al. Improving imaging quality of real-time Fourier single-pixel imaging via deep learning. Sensors, 19, 4190(2019). https://doi.org/10.3390/s19194190

[114] X. Yang et al. High imaging quality of Fourier single pixel imaging based on generative adversarial networks at low sampling rate. Opt. Laser. Eng., 140, 106533(2021). https://doi.org/10.1016/j.optlaseng.2021.106533

[115] X. Tian et al. Interpretable Poisson optimization-inspired deep network for single-photon counting image denoising. IEEE Trans. Instrum. Meas., 72, 1(2022). https://doi.org/10.1109/TIM.2022.3228266

[116] Y. Guan et al. Single photon counting compressive imaging based on a sampling and reconstruction integrated deep network. Opt. Commun., 459, 124923(2020). https://doi.org/10.1016/j.optcom.2019.124923

[117] W. C. Li et al. Deep-learning-based single-photon-counting compressive imaging via jointly trained subpixel convolution sampling. Appl. Opt., 59, 6828(2020). https://doi.org/10.1364/AO.394410

[118] W. Gao et al. Single photon counting compressive imaging using a generative model optimized via sampling and transfer learning. Opt. Express, 29, 5552(2021). https://doi.org/10.1364/OE.413925

[119] Q. Zhao et al. Speckle-based optical cryptosystem and its application for human face recognition via deep learning. Adv. Sci., 9, 2202407(2022). https://doi.org/10.1002/advs.202202407

[120] P. Qi et al. A symmetric forward-inverse reinforcement framework for image reconstruction through scattering media. Opt. Laser Technol., 179, 111222(2024). https://doi.org/10.1016/j.optlastec.2024.111222

[121] X. Lu et al. Multi-polarization fusion network for ghost imaging through dynamic scattering media. Adv. Imaging, 1, 031001(2024). https://doi.org/10.3788/AI.2024.10014

[122] M. Lyu et al. Exploit imaging through opaque wall via deep learning(2017).

[123] Y. LeCun et al. Deep learning. Nature, 521, 436(2015). https://doi.org/10.1038/nature14539

[124] A. Turpin et al. Light scattering control in transmission and reflection with neural networks. Opt. Express, 26, 30911(2018). https://doi.org/10.1364/OE.26.030911

[125] Y. Zhang et al. Machine learning based adaptive optics for doughnut-shaped beam. Opt. Express, 27, 16871(2019). https://doi.org/10.1364/OE.27.016871

[126] S. Cheng et al. Artificial intelligence-assisted light control and computational imaging through scattering media. J. Innov. Opt. Heal. Sci., 12, 1930006(2019). https://doi.org/10.1142/S1793545819300064

[127] B. Rahmani et al. Multimode optical fiber transmission with a deep learning network. Light Sci. Appl., 7, 69(2018). https://doi.org/10.1038/s41377-018-0074-1

[128] Z. Yu et al. Wavefront shaping: a versatile tool to conquer multiple scattering in multidisciplinary fields. Innovation, 3, 100292(2022). https://doi.org/10.1016/j.xinn.2022.100292

[129] Q. Luo et al. Motion-based coherent optical imaging in heavily scattering random media. Opt. Lett., 44, 2716(2019). https://doi.org/10.1364/OL.44.002716

[130] O. Katz et al. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics, 8, 784(2014). https://doi.org/10.1038/nphoton.2014.189

[131] H. Yilmaz et al. Speckle correlation resolution enhancement of wide-field fluorescence imaging. Optica, 2, 424(2015). https://doi.org/10.1364/OPTICA.2.000424

[132] A. Porat et al. Widefield lensless imaging through a fiber bundle via speckle correlations. Opt. Express, 24, 16835(2016). https://doi.org/10.1364/OE.24.016835

[133] Y. Jin et al. Machine learning guided rapid focusing with sensor-less aberration corrections. Opt. Express, 26, 30162(2018). https://doi.org/10.1364/OE.26.030162

[134] Y. Jin et al. Wavefront reconstruction based on deep transfer learning for microscopy. Opt. Express, 28, 20738(2020). https://doi.org/10.1364/OE.396321

[135] Q. Tian et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system. Opt. Express, 27, 10765(2019). https://doi.org/10.1364/OE.27.010765

[136] A. B. Siddik et al. Deep learning estimation of modified zernike coefficients and recovery of point spread functions in turbulence. Opt. Express, 31, 22903(2023). https://doi.org/10.1364/OE.493229

[137] T. Ando et al. Speckle-learning-based object recognition through scattering media. Opt. Express, 23, 33902(2015). https://doi.org/10.1364/OE.23.033902

[138] R. Horisaki et al. Learning-based imaging through scattering media. Opt. Express, 24, 13738(2016). https://doi.org/10.1364/OE.24.013738

[139] R. Horisaki et al. Learning-based focusing through scattering media. Appl. Opt., 56, 4358(2017). https://doi.org/10.1364/AO.56.004358

[140] E. Guo et al. Learning-based method to reconstruct complex targets through scattering medium beyond the memory effect. Opt. Express, 28, 2433(2020). https://doi.org/10.1364/OE.383911

[141] Y. Wang et al. High performance “non-local” generic face reconstruction model using the lightweight Speckle-Transformer (SpT) UNet. Opto-Electron. Adv., 6, 220049-1(2023). https://doi.org/10.29026/oea.2023.220049

[142] T. Eboli et al. End-to-end interpretable learning of non-blind image deblurring, 314(2020).

[143] D. Gong et al. Learning deep gradient descent optimization for image deconvolution. IEEE Trans. Neural Networks Learn. Syst., 31, 5468(2020). https://doi.org/10.1109/TNNLS.2020.2968289

[144] K. Zhang et al. Learning deep CNN denoiser prior for image restoration, 2808(2017).

[145] J. W. Zhang et al. Learning fully convolutional networks for iterative non-blind deconvolution, 6969(2017).

[146] W. S. Zhang et al. Denoising prior driven deep neural network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell., 41, 2305(2019). https://doi.org/10.1109/TPAMI.2018.2873610

[147] Y. Quan et al. Nonblind image deblurring via deep learning in complex field. IEEE Trans. Neural Networks Learn. Syst., 33, 5387(2021). https://doi.org/10.1109/TNNLS.2021.3070596

[148] L. Chen et al. Learning a non-blind deblurring network for night blurry images, 10542(2021).

[149] E. Edrei et al. Optical imaging through dynamic turbid media using the Fourier-domain shower-curtain effect. Optica, 3, 71(2016). https://doi.org/10.1364/OPTICA.3.000071

[150] M. Zhou et al. Deep-learning-based rapid imaging through scattering media beyond the memory effect. IEEE Photonics Technol. Lett., 34, 295(2022). https://doi.org/10.1109/LPT.2022.3153665

[151] S. Zheng et al. Incoherent imaging through highly nonstatic and optically thick turbid media based on neural network. Photon. Res., 9, B220(2021). https://doi.org/10.1364/PRJ.416246

[152] J. Zhao et al. Deep-learning cell imaging through Anderson localizing optical fiber. Adv. Photonics, 1, 066001(2019). https://doi.org/10.1117/1.AP.1.6.066001

[153] P. Fan et al. Deep learning the high variability and randomness inside multimode fibers. Opt. Express, 27, 20241(2019). https://doi.org/10.1364/OE.27.020241

[154] Y. Sun et al. Image reconstruction through dynamic scattering media based on deep learning. Opt. Express, 27, 16032(2019). https://doi.org/10.1364/OE.27.016032

[155] H. Wu et al. Single shot real-time high resolution imaging through dynamic turbid media based on deep learning. Opt. Laser. Eng., 149, 106819(2022). https://doi.org/10.1016/j.optlaseng.2021.106819

[156] S. Zhu et al. Imaging through unknown scattering media based on physics-informed learning. Photon. Res., 9, B210(2021). https://doi.org/10.1364/PRJ.416551

[157] W. Tahir et al. Adaptive 3D descattering with dynamic synthesis network. Light Sci. Appl., 11, 42(2022). https://doi.org/10.1038/s41377-022-00730-x

[158] M. O’Toole et al. Confocal non-line-of-sight imaging based on the light-cone transform. Nature, 555, 338(2018). https://doi.org/10.1038/nature25489

[159] X. Liu et al. Non-line-of-sight imaging using phasor-field virtual wave optics. Nature, 572, 620(2019). https://doi.org/10.1038/s41586-019-1461-3

[160] D. Faccio et al. Non-line-of-sight imaging. Nat. Rev. Phys., 2, 318(2020). https://doi.org/10.1038/s42254-020-0174-8

[161] C. Pei et al. Dynamic non-line-of-sight imaging system based on the optimization of point spread functions. Opt. Express, 29, 32349(2021). https://doi.org/10.1364/OE.439372

[162] T. Li et al. Non-line-of-sight fast tracking in a corridor. Opt. Express, 29, 41568(2021). https://doi.org/10.1364/OE.443366

[163] B. Wang et al. Non-line-of-sight imaging with picosecond temporal resolution. Phy. Rev. Lett., 127, 053602(2021). https://doi.org/10.1103/PhysRevLett.127.053602

[164] R. Geng et al. Passive non-line-of-sight imaging using optimal transport. IEEE Trans. Image Process., 31, 110(2021). https://doi.org/10.1109/TIP.2021.3128312

[165] M. Batarseh et al. Passive sensing around the corner using spatial coherence. Nat. Commun., 9, 3629(2018). https://doi.org/10.1038/s41467-018-05985-w

[166] K. Tanaka et al. Polarized non-line-of-sight imaging, 2136(2020).

[167] C. Zhou et al. Non-line-of-sight imaging off a Phong surface through deep learning(2020).

[168] R. Ramesh et al. 5d time-light transport matrix: what can we reason about scene properties?. Acta Leprologica, 94, 91411(2018).

[169] A. B. Yedidia et al. Using unknown occluders to recover hidden scenes, 12231(2019).

[170] C. A. Metzler et al. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica, 7, 63(2020). https://doi.org/10.1364/OPTICA.374026

[171] W. Chen et al. Learned feature embeddings for non-line-of-sight imaging and recognition. ACM ToG, 39, 1(2020). https://doi.org/10.1145/3414685.3417825

[172] B. Ahn et al. Convolutional approximations to the general non-line-of-sight imaging operator, 7889(2019).

[173] X. Liu et al. Phasor field diffraction based reconstruction for fast non-line-of-sight imaging systems. Nat. Commun., 11, 1645(2020). https://doi.org/10.1038/s41467-020-15157-4

[174] J. T. Ye et al. Compressed sensing for active non-line-of-sight imaging. Opt. Express, 29, 1749(2021). https://doi.org/10.1364/OE.413774

[175] O. Katz et al. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photon., 8, 784(2014). https://doi.org/10.1038/nphoton.2014.189

[176] C. Saunders et al. Computational periscopy with an ordinary digital camera. Nature, 565, 472(2019). https://doi.org/10.1038/s41586-018-0868-6

[177] D. B. Lindell et al. Wave-based non-line-of-sight imaging using fast f-k migration. ACM ToG, 38, 1(2019). https://doi.org/10.1145/3306346.3322937

[178] C. Wu et al. Non–line-of-sight imaging over 1.43 km. Natl. Acad. Sci., 118, e2024468118(2021). https://doi.org/10.1073/pnas.2024468118

[179] C. Jin et al. Richardson–Lucy deconvolution of time histograms for high-resolution non-line-of-sight imaging based on a back-projection method. Opt. Lett., 43, 5885(2018). https://doi.org/10.1364/OL.43.005885

[180] V. Arellano et al. Fast back-projection for non-line of sight reconstruction, 1(2017).

[181] D. B. Lindell et al. Acoustic non-line-of-sight imaging, 6780(2019).

[182] J. B. Boger-Lombard et al. Passive optical time-of-flight for non line-of-sight localization. Nat. Commun., 10, 3343(2019). https://doi.org/10.1038/s41467-019-11279-6

[183] J. Bertolotti et al. Non-invasive imaging through opaque scattering layers. Nature, 491, 232(2012). https://doi.org/10.1038/nature11578

[184] T. Maeda et al. Thermal non-line-of-sight imaging, 1(2019).

[185] M. Kaga et al. Thermal non-line-of-sight imaging from specular and diffuse reflections. IPSJ Trans. Comput. Vis. Appl., 11, 1(2019). https://doi.org/10.1186/s41074-019-0060-4

[186] J. A. Teichman et al. Phasor field waves: a mathematical treatment. Opt. Express, 27, 27500(2019). https://doi.org/10.1364/OE.27.027500

[187] S. A. Reza et al. Phasor field waves: experimental demonstrations of wave-like properties. Opt. Express, 27, 32587(2019). https://doi.org/10.1364/OE.27.032587

[188] J. Dove et al. Paraxial theory of phasor-field imaging. Opt. Express, 27, 18016(2019). https://doi.org/10.1364/OE.27.018016

[189] F. Heide et al. Non-line-of-sight imaging with partial occluders and surface normals. ACM ToG, 38, 1(2019). https://doi.org/10.1145/3269977

[190] F. Xu et al. Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging. Opt. Express, 26, 9945(2018). https://doi.org/10.1364/OE.26.009945

[191] S. W. Seidel et al. Corner occluder computational periscopy: estimating a hidden scene from a single photograph, 1(2019).

[192] S. I. Young et al. Non-line-of-sight surface reconstruction using the directional light-cone transform, 1407(2020).

[193] C. Y. Tsai et al. Beyond volumetric albedo--a surface optimization framework for non-line-of-sight imaging, 1545(2019).

[194] O. Gupta et al. Reconstruction of hidden 3D shapes using diffuse reflections. Opt. Express, 20, 19096(2012). https://doi.org/10.1364/OE.20.019096

[195] D. Wu et al. Frequency analysis of transient light transport with applications in bare sensor imaging, 542(2012).

[196] J. Tasinkevych et al. Circular radon transform inversion technique in synthetic aperture ultrasound imaging: an ultrasound phantom evaluation. Arch. Acoust., 39, 569(2014). https://doi.org/10.2478/aoa-2014-0061

[197] M. Laurenzis et al. Feature selection and back-projection algorithms for nonline-of-sight laser–gated viewing. J. Electron. Imaging, 23, 063003(2014). https://doi.org/10.1117/1.JEI.23.6.063003

[198] M. Tancik et al. Data-driven non-line-of-sight imaging with a traditional camera, IW2B.6(2018).

[199] W. Chen et al. Steady-state non-line-of-sight imaging, 6790(2019).

[200] P. Caramazza et al. Neural network identification of people hidden from view with a single-pixel, single-photon detector. Sci. Rep., 8, 11945(2018). https://doi.org/10.1038/s41598-018-30390-0

[201] A. Kirmani et al. Looking around the corner using transient imaging, 159(2009).

[202] A. Kadambi et al. Occluded imaging with time-of-flight sensors. ACM ToG, 35, 1(2016). https://doi.org/10.1145/2836164

[203] M. Isogawa et al. Optical non-line-of-sight physics-based 3d human pose estimation, 7013(2020).

[204] S. Shen et al. Non-line-of-sight imaging via neural transient fields. IEEE Trans. Pattern Anal. Mach. Intell., 43, 2257(2021). https://doi.org/10.1109/TPAMI.2021.3076062

[205] J. Grau et al. Occlusion fields: an implicit representation for non-line-of-sight surface reconstruction(2022).

[206] H. Liu et al. PI-NLOS: polarized infrared non-line-of-sight imaging. Opt. Express, 31, 44113(2023). https://doi.org/10.1364/OE.507875

[207] Y. Cao et al. Dynamic-excitation-based steady-state non-line-of-sight imaging via multi-branch convolutional neural network. Opt. Laser. Eng., 161, 107369(2023). https://doi.org/10.1016/j.optlaseng.2022.107369

[208] I. Freund et al. Memory effects in propagation of optical waves through disordered media. Phys. Rev. Lett., 61, 2328(1988). https://doi.org/10.1103/PhysRevLett.61.2328

[209] H. Liu et al. Physical picture of the optical memory effect. Photon. Res., 7, 1323(2019). https://doi.org/10.1364/PRJ.7.001323

[210] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758(1982). https://doi.org/10.1364/AO.21.002758

[211] Y. C. Shechtman et al. Phase retrieval with application to optical imaging: a contemporary overview. IEEE Signal Process Mag., 32, 87(2015). https://doi.org/10.1109/MSP.2014.2352673

[212] H. H. Bauschke et al. Hybrid projection–reflection method for phase retrieval. JOSA A, 20, 1025(2003). https://doi.org/10.1364/JOSAA.20.001025

[213] S. Marchesini et al. Alternating projection, ptychographic imaging and phase synchronization. Appl. Comput. Harmon. A., 41, 815(2016). https://doi.org/10.1016/j.acha.2015.06.005

[214] S. Zheng et al. Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach. Opt. Express, 29, 40091(2021). https://doi.org/10.1364/OE.443127

[215] X. He et al. Passive non-line-of-sight imaging reconstruction based on dual input U-Net, 337(2023).

[216] X. Yu et al. CAGAN: a channel-aware generative adversarial network for passive non-line-of-sight imaging, 508(2023).

[217] Z. Wang et al. Non-line-of-sight imaging and location determination using deep learning. Opt. Laser. Eng., 169, 107701(2023). https://doi.org/10.1016/j.optlaseng.2023.107701

[218] C. Wang et al. Passive non-line-of-sight imaging of moving targets using physical embedding and event-based vision(2024).

[219] S. Wu et al. Physics-constrained deep-inverse point spread function model: toward non-line-of-sight imaging reconstruction. Adv. Photonics Nexus, 3, 026010(2024). https://doi.org/10.1117/1.APN.3.2.026010

[220] Y. Xu et al. Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access, 4, 165(2015). https://doi.org/10.1109/ACCESS.2015.2511558

[221] Y. Guo et al. Haze visibility enhancement for promoting traffic situational awareness in vision-enabled intelligent transportation. IEEE Trans. Veh. Technol., 72, 15421(2023). https://doi.org/10.1109/TVT.2023.3298041

[222] K. Nguyen et al. Analysis of the influence of de-hazing methods on vehicle detection in aerial images. Int. J. Adv. Comput. Sci. Appl., 13, 6(2022). https://doi.org/10.14569/IJACSA.2022.01306100

[223] J. Liu et al. A review of remote sensing image dehazing. Sensors, 21, 3926(2021). https://doi.org/10.3390/s21113926

[224] S. Karavarsamis et al. A survey of deep learning-based image restoration methods for enhancing situational awareness at disaster sites: the cases of rain, snow and haze. Sensors, 22, 4707(2022). https://doi.org/10.3390/s22134707

[225] Y. Song et al. Optical imaging and image restoration techniques for deep ocean mapping: a comprehensive survey. J. Photogramm. Remote Sens. Geoinf. Sci., 90, 243(2022). https://doi.org/10.1007/s41064-022-00206-y

[226] M. J. Islam et al. Fast underwater image enhancement for improved visual perception. IEEE Rob. Autom. Lett., 5, 3227(2020). https://doi.org/10.1109/LRA.2020.2974710

[227] Y. Y. Schechner et al. Instant dehazing of images using polarization, 1(2001).

[228] T. Treibitz et al. Active polarization descattering. IEEE Trans. Pattern Anal. Mach. Intell., 31, 385(2008). https://doi.org/10.1109/TPAMI.2008.85

[229] Y. Y. Schechner et al. Polarization-based vision through haze. Appl. Opt., 42, 511(2003). https://doi.org/10.1364/AO.42.000511

[230] C. Lei et al. Shape from polarization for complex scenes in the wild, 12632(2022).

[231] J. Zhang et al. Learning a convolutional demosaicing network for microgrid polarimeter imagery. Opt. Lett., 43, 4534(2018). https://doi.org/10.1364/OL.43.004534

[232] Y. Shi et al. Polarization-based haze removal using self-supervised network. Front. Phys-lausanne, 9, 789232(2022). https://doi.org/10.3389/fphy.2021.789232

[233] Y. Lyu et al. Reflection separation using a pair of unpolarized and polarized images, 32(2019).

[234] T. Liu et al. Deep learning-based holographic polarization microscopy. ACS Photonics, 7, 3023(2020). https://doi.org/10.1021/acsphotonics.0c01051

[235] J. Zhang et al. PFNet: an unsupervised deep network for polarization image fusion. Opt. Lett., 45, 1507(2020). https://doi.org/10.1364/OL.384189

[236] J. Zhang et al. Visible light polarization image desmogging via cycle convolutional neural network. Multimedia Syst., 28, 45(2022). https://doi.org/10.1007/s00530-021-00802-9

[237] K. Suo et al. Image dehazing combining polarization properties and deep learning. JOSA A, 41, 311(2024). https://doi.org/10.1364/JOSAA.507892

[238] G. N. Bailey et al. Archaeology of the continental shelf: marine resources, submerged landscapes and underwater archaeology. Quaternary Sci. Rev., 27, 2153(2008). https://doi.org/10.1016/j.quascirev.2008.08.012

[239] L. B. Wolff. Polarization vision: a new sensory approach to image understanding. Image Vision Comput., 15, 81(1997). https://doi.org/10.1016/S0262-8856(96)01123-7

[240] L. Shen et al. An iterative image dehazing method with polarization. IEEE Trans. Multimedia, 21, 1093(2018). https://doi.org/10.1109/TMM.2018.2871955

[241] Y. Y. Schechner et al. Recovery of underwater visibility and structure by polarization analysis. IEEE J. Oceanic Eng., 30, 570(2005). https://doi.org/10.1109/JOE.2005.850871

[242] X. Li et al. Polarimetric image recovery method combining histogram stretching for underwater imaging. Sci. Rep., 8, 12430(2018). https://doi.org/10.1038/s41598-018-30566-8

[243] T. Liu et al. Polarimetric underwater image recovery for color image with crosstalk compensation. Opt. Laser. Eng., 124, 105833(2020). https://doi.org/10.1016/j.optlaseng.2019.105833

[244] J. Guan et al. Target detection in turbid medium using polarization-based range-gated technology. Opt. Express, 21, 14152(2013). https://doi.org/10.1364/OE.21.014152

[245] J. Liang et al. Method for enhancing visibility of hazy images based on polarimetric imaging. Photon. Res., 2, 38(2014). https://doi.org/10.1364/PRJ.2.000038

[246] Y. Li et al. Deep learning approach to scalable imaging through scattering media(2019).

[247] H. Hu et al. Polarimetric underwater image recovery via deep learning. Opt. Laser. Eng., 133, 106152(2020). https://doi.org/10.1016/j.optlaseng.2020.106152

[248] H. Liu et al. Review of polarimetric image denoising. Adv. Imaging, 1, 022001(2024). https://doi.org/10.3788/AI.2024.20001

[249] H. Hu et al. IPLNet: a neural network for intensity-polarization imaging in low light. Opt. Lett., 45, 6162(2020). https://doi.org/10.1364/OL.409673

[250] Y. Xiang et al. Underwater polarization imaging recovery based on polarimetric residual dense network. IEEE Photon. J., 14, 7860206(2022). https://doi.org/10.1109/JPHOT.2022.3221726

[251] Q. Ren et al. The underwater polarization dehazing imaging with a lightweight convolutional neural network. Optik, 251, 168381(2022). https://doi.org/10.1016/j.ijleo.2021.168381

[252] J. Gao et al. Mueller transform matrix neural network for underwater polarimetric dehazing imaging. Opt. Express, 31, 27213(2023). https://doi.org/10.1364/OE.496978

[253] H. Lin et al. Single image deblurring for pulsed laser range-gated imaging system with multi-slice Integration. Photonics, 9, 642(2022). https://doi.org/10.3390/photonics9090642

[254] Y. Zhang et al. Mask-guided deep learning fishing net detection and recognition based on underwater range gated laser imaging. Opt. Laser Technol., 171, 110402(2024). https://doi.org/10.1016/j.optlastec.2023.110402

[255] T. Gruber et al. Gated2depth: Real-time dense lidar from gated images, 1506(2019).

[256] X. Liu et al. Vision-guided three-dimensional range-gated imaging based on epistemic uncertainty estimation. Opt. Eng., 62, 123105(2023). https://doi.org/10.1117/1.OE.62.12.123105

[257] C. Xia et al. Range-intensity-profile-guided gated light ranging and imaging based on a convolutional neural network. Sensors, 24, 2151(2024). https://doi.org/10.3390/s24072151

[258] C. Yang et al. Accelerated photoacoustic tomography reconstruction via recurrent inference machines, 6371(2019).

[259] P. Beard. Biomedical photoacoustic imaging. Interface Focus, 1, 602(2011). https://doi.org/10.1098/rsfs.2011.0028

[260] X. Wei et al. Deep Learning-powered biomedical photoacoustic imaging. Neurocomputing, 573, 127207(2023). https://doi.org/10.1016/j.neucom.2023.127207

[261] S. Gutta et al. Deep neural network-based bandwidth enhancement of photoacoustic data. J. Biomed. Opt., 22, 116001(2017). https://doi.org/10.1117/1.JBO.22.11.116001

[262] S. Antholzer et al. Deep learning for photoacoustic tomography from sparse data. Inverse Probl. Sci. Eng., 27, 987(2019). https://doi.org/10.1080/17415977.2018.1518444

[263] A. Hariri et al. Development of low-cost photoacoustic imaging systems using very low-energy pulsed laser diodes. J. Biomed. Opt., 22, 075001(2017). https://doi.org/10.1117/1.JBO.22.7.075001

[264] S. Antholzer et al. Photoacoustic image reconstruction via deep learning, 10494, 433.

[265] R. Manwar et al. Overview of ultrasound detection technologies for photoacoustic imaging. Micromachines, 11, 692(2020). https://doi.org/10.3390/mi11070692

[266] H. Ma et al. Quantitative and anatomical imaging of dermal angiopathy by noninvasive photoacoustic microscopic biopsy. Bio. Opt. Express, 12, 6300(2021). https://doi.org/10.1364/BOE.439625

[267] H. Ma et al. Multiscale confocal photoacoustic dermoscopy to evaluate skin health. Quant. Imag. Med. Surg., 12, 2696(2022). https://doi.org/10.21037/qims-21-878

[268] H. Ma et al. Three dimensional confocal photoacoustic dermoscopy with an autofocusing sono-opto probe. J. Biophoton., 15, e202100323(2022). https://doi.org/10.1002/jbio.202100323

[269] H. Ma et al. Monitoring of microvascular calcification by time-resolved photoacoustic microscopy. Photoacoustic, 41, 100664(2025). https://doi.org/10.1016/j.pacs.2024.100664

[270] T. Feng et al. Adaptively spatial PSF removal enables contrast enhancement for multi-layer(2024).

[271] Y. Gao et al. 4D spectral-spatial computational photoacoustic dermoscopy. Photoacoustic, 34, 100572(2023). https://doi.org/10.1016/j.pacs.2023.100572

[272] Z. Guan et al. Ptychonet: Fast and High Quality Phase Retrieval for Ptychography(2019).

[273] X. Pan et al. An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model. iScience, 26, 12(2023). https://doi.org/10.1016/j.isci.2023.108420

[274] W. Lv et al. Resolution-enhanced ptychography framework with an equivalent upsampling and precise position. Appl. Opt., 61, 2903(2022). https://doi.org/10.1364/AO.451431

[275] W. Gan et al. PtychoDV: vision transformer-based deep unrolling network for ptychographic image reconstruction. IEEE Open J. Signal Process., 5, 539(2024). https://doi.org/10.1109/OJSP.2024.3375276

[276] M. J. Cherukara et al. AI-enabled high-resolution scanning coherent diffraction imaging. Appl. Phy. Lett., 117, 4(2020). https://doi.org/10.1063/5.0013065

[277] V. Bianco et al. Deep learning-based, misalignment resilient, real-time Fourier Ptychographic Microscopy reconstruction of biological tissue slides. IEEE J. Sel. Top. Quantum Electron., 28, 6800110(2022). https://doi.org/10.1109/JSTQE.2022.3154236

[278] Q. Chen et al. Fourier ptychographic microscopy with untrained deep neural network priors. Opt. Express, 30, 39597(2022). https://doi.org/10.1364/OE.472171

[279] D. Yang et al. Fourier ptychography multi-parameter neural network with composite physical priori optimization. Bio. Opt. Express, 13, 2739(2022). https://doi.org/10.1364/BOE.456380

[280] M. Sun et al. Neural network model combined with pupil recovery for Fourier ptychographic microscopy. Opt. Express, 27, 24161(2019). https://doi.org/10.1364/OE.27.024161

[281] L. Bouchama et al. Fourier ptychographic microscopy image enhancement with bi-modal deep learning. Bio. Opt. Express, 14, 3172(2023). https://doi.org/10.1364/BOE.489776

[282] R. Wang et al. Virtual brightfield and fluorescence staining for Fourier ptychography via unsupervised deep learning. Opt. Lett., 45, 5405(2020). https://doi.org/10.1364/OL.400244

[283] F. Shamshad et al. Subsampled Fourier ptychography via pretrained invertible and untrained network priors(2019).

[284] V. Bianco et al. Deep learning assisted Fourier ptychography for cells and tissue analysis, 12622, 65(2023).

[285] J. Sha et al. Improving the resolution of Fourier ptychographic imaging using an a priori neural network. Opt. Lett., 48, 6316(2023). https://doi.org/10.1364/OL.508134

[286] T. Li et al. Coordinate-based neural network for Fourier phase retrieval, 2585(2024).

[287] L. Boominathan et al. Phase retrieval for Fourier ptychography under varying amount of measurements(2018).

[288] F. Ströhl et al. Object detection neural network improves Fourier ptychography reconstruction. Opt. Express, 28, 37199(2020). https://doi.org/10.1364/OE.409679

[289] L. Bouchama et al. A physics-inspired deep learning framework for an efficient Fourier ptychographic microscopy reconstruction under low overlap conditions. Sensors, 23, 6829(2023). https://doi.org/10.3390/s23156829

[290] Y. F. Cheng et al. Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy. Opt. Express, 27, 644(2019). https://doi.org/10.1364/OE.27.000644

[291] H. Zhou et al. Fourier ptychographic microscopy image stack reconstruction using implicit neural representations. Optica, 10, 1679(2023). https://doi.org/10.1364/OPTICA.505283

[292] S. Jiang et al. Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow. Bio. Opt. Express, 9, 3306(2018). https://doi.org/10.1364/BOE.9.003306

[293] P. Bohra et al. Dynamic Fourier ptychography with deep spatiotemporal priors. Inverse Probl., 39, 064005(2023). https://doi.org/10.1088/1361-6420/acca72

[294] S. Gupta et al. Perceptually driven conditional GAN for Fourier ptychography, 1267(2019).

[295] S. Li et al. Synthetic apertures for array ptychography imaging via deep learning, 12138, 41(2022).

[296] W. Chen. Fourier ptychography based on a u-net convolutional neural network, 130(2023).

[297] H. Wu et al. Super-resolution microscopy reveals new insights into organelle interactions. Adv. Imaging, 1, 032001(2024). https://doi.org/10.3788/AI.2024.20004

[298] F. Zhao et al. Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens. Bio. Opt. Express, 11, 7273(2020). https://doi.org/10.1364/BOE.409732

[299] J. Li et al. Spatial and temporal super-resolution for fluorescence microscopy by a recurrent neural network. Opt. Express, 29, 15747(2021). https://doi.org/10.1364/OE.423892

[300] B. Yao et al. Image reconstruction with a deep convolutional neural network in high-density super-resolution microscopy. Opt. Express, 28, 15432(2020). https://doi.org/10.1364/OE.392358

[301] S. W. Hell et al. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett., 19, 780(1994). https://doi.org/10.1364/OL.19.000780

[302] M. Li et al. Deep adversarial network for super stimulated emission depletion imaging. J. Nanophoton., 14, 016009(2020). http://10.1117/1.JNP.14.016009

[303] Y. I. Chen et al. Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis. Nanoscale, 15, 9449(2023). https://doi.org/10.1039/D3NR00305A

[304] V. Ebrahimi et al. Deep learning enables fast, gentle STED microscopy. Commun. Biol., 6, 674(2023). https://doi.org/10.1038/s42003-023-05054-z

[305] J. W. Goodman. Introduction to Fourier Optics(1996).

[306] U. Schanars et al. Digital holography and wavefront sensing: Principles, Techniques and Applications(2014).

[307] Z. Yu et al. Spin-orbit-locking vectorial metasurface holography. Adv. Mater., 24, 2415142(2024). https://doi.org/10.1002/adma.202415142

[308] T. Nguyen et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Opt. Express, 25, 15043(2017). https://doi.org/10.1364/OE.25.015043

[309] G. Yu et al. Asymmetrical neural network for real-time and high-quality computer-generated holography. Opt. Lett., 48, 5351(2023). https://doi.org/10.1364/OL.497518

[310] M. Liao et al. Scattering imaging as a noise removal in digital holography by using deep learning. New J. Phys., 24, 083014(2022). https://doi.org/10.1088/1367-2630/ac8308

[311] K. Wang et al. One-step robust deep learning phase unwrapping. Opt. Express, 27, 15100(2019). https://doi.org/10.1364/OE.27.015100

[312] H. Li et al. Deep DIH: single-shot digital in-line holography reconstruction by deep learning. IEEE Access, 8, 202648(2020). https://doi.org/10.1109/ACCESS.2020.3036380

[313] J. Wu et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt. Lett., 46, 2908(2021). https://doi.org/10.1364/OL.425485

[314] J. Di et al. Quantitative phase imaging using deep learning-based holographic microscope. Front. Phys., 9, 651313(2021). https://doi.org/10.3389/fphy.2021.651313

[315] M. R. Teague. Deterministic phase retrieval: a Green’s function solution. JOSA, 73, 1434(1983). https://doi.org/10.1364/JOSA.73.001434

[316] K. Wang et al. Transport of intensity equation from a single intensity image via deep learning. Opt. Laser. Eng., 134, 106233(2020). https://doi.org/10.1016/j.optlaseng.2020.106233

[317] F. Wang et al. Phase imaging with an untrained neural network. Light Sci. Appl., 9, 77(2020). https://doi.org/10.1038/s41377-020-0302-3

[318] Y. Jin et al. Neural-field-assisted transport-of-intensity phase microscopy: partially coherent quantitative phase imaging under unknown defocus distance. Photon. Res., 12, 1494(2024). https://doi.org/10.1364/PRJ.521056

[319] A. Maric et al. Underwater optical imaging and sensing in turbidity using three-dimensional integral imaging: a review. Adv. Imaging, 2, 012001(2024). https://doi.org/10.3788/AI.2025.20002

[320] X. Lu et al. Learning video object segmentation from unlabeled videos, 8960(2020).

[321] R. Yao et al. Video object segmentation and tracking: a survey. ACM TIST, 11, 1(2020). https://doi.org/10.1145/3391743

[322] G. Ciaparrone et al. Deep learning in video multi-object tracking: a survey. Neurocomputing, 381, 61(2020). https://doi.org/10.1016/j.neucom.2019.11.023

[323] X. Lu et al. Adaptive region proposal with channel regularization for robust object tracking. IEEE Trans. Circuits Syst. Video Technol., 31, 1268(2019). https://doi.org/10.1109/TCSVT.2019.2944654

[324] H. Altwaijry et al. Learning to detect and match keypoints with deep architectures(2016).

[325] L. Jiao et al. A survey of deep learning-based object detection. IEEE Access, 7, 128837(2019). https://doi.org/10.1109/ACCESS.2019.2939201

[326] A. Krizhevsky et al. ImageNet classification with deep convolutional neural networks. Commun. ACM, 60, 84(2017). https://doi.org/10.1145/3065386

[327] S. Zhang. High-speed 3D shape measurement with structured light methods: a review. Opt. Laser. Eng., 106, 119(2018). https://doi.org/10.1016/j.optlaseng.2018.02.017

[328] E. R. Eiríksson et al. Precision and accuracy parameters in structured light 3-D scanning, XL-5/W8, 7(2016).

[329] T. Zhang et al. Three-dimensional measurement using structured light based on deep learning. Comput. Syst. Sci. Eng., 36, 271(2021). https://doi.org/10.32604/csse.2021.014181

[330] Y. R. Guo et al. Method for extracting line structured light center in complex environment. Comput. Eng. Design, 40, 1133(2019).

[331] S. Feng et al. Fringe pattern analysis using deep learning. Adv. Photon., 1, 025001(2019). https://doi.org/10.1117/1.AP.1.2.025001

[332] J. Qian et al. Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. APL Photon., 5, 4(2020). https://doi.org/10.1063/5.0003217

[333] J. Qian et al. Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt. Lett., 45, 1842(2020). https://doi.org/10.1364/OL.388994

[334] Y. Li et al. Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement. Opto-Electron. Adv., 5, 210021-1(2022). https://doi.org/10.29026/oea.2022.210021

[335] S. Van der Jeught et al. Deep neural networks for single shot structured light profilometry. Opt. Express, 27, 17091(2019). https://doi.org/10.1364/OE.27.017091

[336] H. Nguyen et al. Single-shot 3D shape reconstruction using structured light and deep convolutional neural networks. Sensors, 20, 3718(2020). https://doi.org/10.3390/s20133718

[337] H. Nguyen et al. hNet: single-shot 3D shape reconstruction using structured light and h-shaped global guidance network. Results Opt., 4, 100104(2021). https://doi.org/10.1016/j.rio.2021.100104

[338] H. Nguyen et al. MIMONet: structured-light 3D shape reconstruction by a multi-input multi-output network. Appl. Opt., 60, 5134(2021). https://doi.org/10.1364/AO.426189

[339] A. H. Nguyen et al. Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning. Appl. Opt., 61, 10105(2022). https://doi.org/10.1364/AO.468984

[340] R. C. Machineni et al. End-to-end deep learning-based fringe projection framework for 3D profiling of objects. Comput. Vis. Image Underst., 199, 103023(2020). https://doi.org/10.1016/j.cviu.2020.103023

[341] L. Wang et al. 3D reconstruction from structured-light profilometry with dual-path hybrid network. Eurasip J. Adv. Sig. Process., 2022, 14. https://doi.org/10.1186/s13634-022-00848-5

[342] T. Jia et al. Depth measurement based on a convolutional neural network and structured light. Meas. Sci. Technol., 33, 025202(2021). https://doi.org/10.1088/1361-6501/ac329d

[343] S. Feng et al. Deep-learning-based fringe-pattern analysis with uncertainty estimation. Optica, 8, 1507(2021). https://doi.org/10.1364/OPTICA.434311

[344] W. Yin et al. Physics-informed deep learning for fringe pattern analysis. Opto-Electron. Adv., 7, 230034(2024). https://doi.org/10.29026/oea.2024.230034

[345] W. Chen et al. Deep-learning-enabled temporally super-resolved multiplexed fringe projection profilometry: high-speed kHz 3D imaging with low-speed camera. PhotoniX, 5, 25(2024). https://doi.org/10.1186/s43074-024-00139-2

[346] J. Beltrán et al. Birdnet: a 3d object detection framework from lidar information, 3517(2018).

[347] A. Barrera et al. Birdnet+: end-to-end 3d object detection in lidar bird’s eye view, 3517(2020).

[348] A. Barrera et al. Birdnet+: two-stage 3d object detection in lidar through a sparsity-invariant bird’s eye view. IEEE Access, 9, 160299(2021). https://doi.org/10.1109/ACCESS.2021.3131389

[349] C. R. Qi et al. Pointnet: deep learning on point sets for 3d classification and segmentation, 652(2017).

[350] M. Tatarchenko et al. Octree generating networks: efficient convolutional architectures for high-resolution 3d outputs, 2088(2017).

[351] Y. Yao et al. Mvsnet: depth inference for unstructured multi-view stereo, 767(2018).

[352] F. Luo et al. Sparse RGB-D images create a real thing: a flexible voxel based 3D reconstruction pipeline for single object. Visual Inform., 7, 66(2023). https://doi.org/10.1016/j.visinf.2022.12.002

[353] G. Xu et al. Attention concatenation volume for accurate and efficient stereo matching, 12981(2022).

[354] W. Zhan et al. A semi-supervised method for patchmatch multi-view stereo with sparse points. Photonics, 9, 983(2022). https://doi.org/10.3390/photonics9120983

[355] R. Shao et al. Diffustereo: high quality human reconstruction via diffusion-based stereo using sparse cameras, 702(2022).

[356] D. Zheng et al. Diffuvolume: diffusion model for volume based stereo matching(2023).

[357] K. Koshikawa. A polarimetric approach to shape understanding of glossy objects. Adv. Robotics, 2, 190(1979).

[358] M. Saito et al. Measurement of surface orientations of transparent objects by use of polarization in highlight. JOSA A, 16, 2286(1999). https://doi.org/10.1364/JOSAA.16.002286

[359] R. B. Fisher. From Surfaces to Objects: Computer Vision and Three Dimensional Scene Analysis(1989).

[360] A. M. Wallace et al. Improving depth image acquisition using polarized light. Int. J. Comput. Vis., 32, 87(1999). https://doi.org/10.1023/A:1008154415349

[361] V. Müller. Elimination of specular surface-reflectance using polarized and unpolarized light, 4, 625(1996).

[362] Y. Zhao et al. Multi-Band Polarization Imaging(2016).

[363] D. Miyazaki et al. Surface normal estimation of black specular objects from multiview polarization images. Opt. Eng., 56, 041303(2017). https://doi.org/10.1117/1.OE.56.4.041303

[364] Z. Cui et al. Polarimetric multi-view stereo, 1558(2017).

[365] W. A. P. Smith et al. Linear depth estimation from an uncalibrated, monocular polarisation image, 14, 109(2016).

[366] A. H. Mahmoud et al. Direct method for shape recovery from polarization and shading, 1769(2012).

[367] A. Kadambi et al. Polarized 3d: synthesis of polarization and depth cues for enhanced 3d sensing, 1(2015).

[368] A. Kadambi et al. Depth sensing using geometrically constrained polarization normals. Int. J. Comput. Vis., 125, 34(2017). https://doi.org/10.1007/s11263-017-1025-7

[369] Y. Ba et al. Deep shape from polarization, 16, 554(2020).

[370] Y. Kondo et al. Accurate polarimetric BRDF for real polarization scene rendering, 16, 220(2020).

[371] V. Deschaintre et al. Deep polarization imaging for 3d shape and svbrdf acquisition, 15567(2021).

[372] J. Hur et al. Self-supervised monocular scene flow estimation, 7396(2020).

[373] P. Han et al. Accurate passive 3D polarization face reconstruction under complex conditions assisted with deep learning. Photonics, 9, 924(2022). https://doi.org/10.3390/photonics9120924

[374] X. Wu et al. Three dimensional shape reconstruction via polarization imaging and deep learning. Sensors, 23, 4592(2023). https://doi.org/10.3390/s23104592

[375] X. Li et al. Multi-target distortion correction in 3D shape from polarization using a monocular camera system by deep neural networks. Opt. Lett., 48, 5053(2023). https://doi.org/10.1364/OL.499161

[376] T. Huang et al. Learning accurate 3d shape based on stereo polarimetric imaging, 17287(2023).

[377] R. Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 580(2014).

[378] R. Girshick. Fast r-cnn, 1440(2015).

[379] S. Ren et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137(2016). https://doi.org/10.1109/TPAMI.2016.2577031

[380] J. Redmon et al. You only look once: unified, real-time object detection, 779(2016).

[381] W. Liu et al. SSD: Single shot multibox detector, 14, 21(2016).

[382] H. Law et al. Cornernet: detecting objects as paired keypoints, 734(2018).

[383] L. B. Wolff. Polarization-based material classification from specular reflection. IEEE Trans. Pattern Anal. Mach. Intell., 12, 1059(1990). https://doi.org/10.1109/34.61705

[384] L. B. Wolff. Surface orientation from polarization images, 850, 110(1988).

[385] D. H. Goldstein. Polarized Light(2017).

[386] J. S. Lee et al. Polarimetric Radar Imaging: From Basics to Applications(2017).

[387] J. S. Tyo et al. Target detection in optically scattering media by polarization-difference imaging. Appl. Opt., 35, 1855(1996). https://doi.org/10.1364/AO.35.001855

[388] W. Fan et al. Polarization-based car detection, 3069(2018).

[389] Y. Pang et al. Progressive polarization based reflection removal via realistic training data generation. Pattern Recogn., 124, 108497(2022). https://doi.org/10.1016/j.patcog.2021.108497

[390] A. Tan et al. Object detection based on polarization image fusion and grouped convolutional attention network. Vis. Comput., 40, 3199(2024). https://doi.org/10.1007/s00371-023-03022-6

[391] Y. Tian et al. Face anti-spoofing by learning polarization cues in a real-world scenario, 129(2020).

[392] K. Usmani et al. Deep learning polarimetric three-dimensional integral imaging object recognition in adverse environmental conditions. Opt. Express, 29, 12215(2021). https://doi.org/10.1364/OE.421287

[393] J. Huang et al. Snapshot polarization-sensitive holography for detecting microplastics in turbid water. ACS Photonics, 10, 4483(2023). https://doi.org/10.1021/acsphotonics.3c01350

[394] R. Wang et al. Detection and classification of cotton foreign fibers based on polarization imaging and improved YOLOv5. Sensors, 23, 4415(2023). https://doi.org/10.3390/s23094415

[395] K. Yang et al. Predicting polarization beyond semantics for wearable robotics, 96(2018).

[396] Y. Zhang et al. Exploration of deep learning-based multimodal fusion for semantic road scene segmentation(2019).

[397] M. Blanchon et al. Outdoor scenes pixel-wise semantic segmentation using polarimetry and fully convolutional network(2019).

[398] Y. Qiao et al. Multi-view spectral polarization propagation for video glass segmentation, 23218(2023).

[399] C. Liu et al. An unsupervised snow segmentation approach based on dual-polarized scattering mechanism and deep neural network. IEEE Trans. Geosci. Remote Sens., 61, 1(2023). https://doi.org/10.1109/TGRS.2023.3262727

[400] G. M. de Souza Moreno et al. Deep semantic segmentation of mangroves in Brazil combining spatial, temporal, and polarization data from Sentinel-1 time series. Ocean Coast. Manage., 231, 106381(2023). https://doi.org/10.1016/j.ocecoaman.2022.106381

[401] H. Farid et al. Separating reflections and lighting using independent components analysis, 262(1999).

[402] Y. Y. Schechner et al. Polarization-based decorrelation of transparent layers: the inclination angle of an invisible surface, 814(1999).

[403] R. Wan et al. Benchmarking single-image reflection removal algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 45, 1424(2022). https://doi.org/10.1109/TPAMI.2022.3168560

[404] A. M. Bronstein et al. “Sparse ICA for blind separation of transmitted and reflected images. Int. J. Imaging Syst. Technol., 15, 84(2005). https://doi.org/10.1002/ima.20042

[405] N. Kong et al. A physically-based approach to reflection separation: from physical modeling to constrained optimization. IEEE Trans. Pattern Anal. Mach. Intell., 36, 209(2013). https://doi.org/10.1109/TPAMI.2013.45

[406] P. Wieschollek et al. Separating reflection and transmission images in the wild, 89(2018).

[407] C. Lei et al. Polarized reflection removal with perfect alignment in the wild, 1750(2020).

[408] X. Xu et al. ColorPolarNet: residual dense network-based chromatic intensity-polarization imaging in low-light environment. IEEE Trans. Instrum. Meas., 71, 1(2022). https://doi.org/10.1109/TIM.2022.3216391

[409] G. C. Sargent et al. Conditional generative adversarial network demosaicing strategy for division of focal plane polarimeters. Opt. Express, 28, 38419(2020). https://doi.org/10.1364/OE.412687

[410] Y. Sun et al. Color polarization demosaicking by a convolutional neural network. Opt. Lett., 46, 4338(2021). https://doi.org/10.1364/OL.431919

[411] M. Pistellato et al. Deep demosaicing for polarimetric filter array cameras. IEEE Trans. Image Process., 31, 2017(2022). https://doi.org/10.1109/TIP.2022.3150296

[412] J. Duan et al. Joint target geometry and polarization properties for polarization image fusion. Opt. Laser. Eng., 178, 108176(2024). https://doi.org/10.1016/j.optlaseng.2024.108176

[413] D. Ivanov et al. Polarization and depolarization metrics as optical markers in support to histopathology of ex vivo colon tissue. Bio. Opt. Express, 12, 4560(2021). https://doi.org/10.1364/BOE.426713

[414] O. Rodríguez-Núñez et al. Polarimetric visualization of healthy brain fiber tracts under adverse conditions: ex vivo studies. Bio. Opt. Express, 12, 6674(2021). https://doi.org/10.1364/BOE.439754

[415] H. R. Lee et al. Mueller matrix imaging for collagen scoring in mice model of pregnancy. Sci. Rep., 11, 15621(2021). https://doi.org/10.1038/s41598-021-95020-8

[416] P. Schucht et al. Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry. IEEE Trans. Med. Imaging, 39, 4376(2020). https://doi.org/10.1109/TMI.2020.3018439

[417] T. Novikova et al. Multi-spectral Mueller matrix imaging polarimetry for studies of human tissues, TTh3B.2(2016).

[418] V. Dremin et al. Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning. IEEE Trans. Med. Imaging, 40, 1207(2021). https://doi.org/10.1109/TMI.2021.3049591

[419] C. Rodríguez et al. Polarimetric data-based model for tissue recognition. Bio. Opt. Express, 12, 4852(2021). https://doi.org/10.1364/BOE.426387

[420] Y. Zhu et al. Probing layered structures by multi-color backscattering polarimetry and machine learning. Bio. Opt. Express, 12, 4324(2021). https://doi.org/10.1364/BOE.425614

[421] M. S. Yousaf et al. Machine assisted classification of chicken, beef and mutton tissues using optical polarimetry and Bagging model. Photodiagn. Photodyn., 31, 101779(2020). https://doi.org/10.1016/j.pdpdt.2020.101779

[422] Y. Quéau et al. Learning to classify materials using Mueller imaging polarimetry, 11172, 246(2019).

[423] T. Huang et al. Artificial intelligence for medicine: progress, challenges, and perspectives. Inno. Med., 1, 2(2023). https://doi.org/10.59717/j.xinn-med.2023.100030

[424] I. J. Vaughn et al. Classification using active polarimetry, 8364, 243(2012).

[425] S. Panigrahi et al. Machine learning techniques used for the histopathological image analysis of oral cancer—a review. Open Bioinformat. J., 13, 106(2020). https://doi.org/10.2174/1875036202013010106

[426] N. T. Luu et al. Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm. J. Bio. Opt., 26, 075001(2021). https://doi.org/10.1088/2040-8986/ad4722

[427] I. Ahmad et al. Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues. Photodiagn. Photodyn., 14, 134(2016). https://doi.org/10.1016/j.pdpdt.2016.04.004

[428] X. Zhou et al. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning, 11603, 165(2021).

[429] S. Mukhopadhyay et al. Optical diagnosis of colon and cervical cancer by support vector machine, 9887, 46(2016).

[430] V. Dremin et al. Optical percutaneous needle biopsy of the liver: a pilot animal and clinical study. Sci. Rep., 10, 14200(2020). http://10.1038/S41598-020-71089-5

[431] E. Zherebtsov et al. Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area. Diagnostics, 10, 873(2020). https://doi.org/10.3390/diagnostics10110873

[432] G. Wang et al. Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images. Bio. Opt. Express, 12, 5658(2021). https://doi.org/10.1364/BOE.429918

[433] Y. Dong et al. Deriving polarimetry feature parameters to characterize microstructural features in histological sections of breast tissues. IEEE Trans. Biomed. Eng., 68, 881(2020). https://doi.org/10.1109/TBME.2020.3019755

[434] X. Li et al. Classification of morphologically similar algae and cyanobacteria using Mueller matrix imaging and convolutional neural networks. Appl. Opt., 56, 6520(2017). https://doi.org/10.1364/AO.56.006520

[435] X. Li et al. Polarimetric learning: a Siamese approach to learning distance metrics of algal Mueller matrix images. Appl. Opt., 57, 3829(2018). https://doi.org/10.1364/AO.57.003829

[436] R. Bi et al. Pulse feature-enhanced classification of microalgae and cyanobacteria using polarized light scattering and fluorescence signals. Biosensors, 14, 160(2024). https://doi.org/10.3390/bios14040160

[437] H. He et al. Mueller matrix polarimetry—an emerging new tool for characterizing the microstructural feature of complex biological specimen. J. Lightwave Technol., 37, 2534(2018). https://doi.org/10.1109/JLT.2018.2868845

[438] Y. Dong et al. Quantitatively characterizing the microstructural features of breast ductal carcinoma tissues in different progression stages by Mueller matrix microscope. Bio. Opt. Express, 8, 3643(2017). https://doi.org/10.1364/BOE.8.003643

[439] T. Liu et al. Distinguishing structural features between Crohn’s disease and gastrointestinal luminal tuberculosis using Mueller matrix derived parameters. J. Biophoton., 12, e201900151(2019). https://doi.org/10.1002/jbio.201900151

[440] Y. Wang et al. Differentiating characteristic microstructural features of cancerous tissues using Mueller matrix microscope. Micron, 79, 8(2015). https://doi.org/10.1016/j.micron.2015.07.014

[441] Y. Wang et al. Mueller matrix microscope: a quantitative tool to facilitate detections and fibrosis scorings of liver cirrhosis and cancer tissues. J. Bio. Opt., 21, 071112(2016). https://doi.org/10.1117/1.JBO.21.7.071112

[442] D. Ivanov et al. Polarization-based histopathology classification of ex vivo colon samples supported by machine learning. Front. Phys., 9, 814787(2022). https://doi.org/10.3389/fphy.2021.814787

[443] Y. Dong et al. A polarization-imaging-based machine learning framework for quantitative pathological diagnosis of cervical precancerous lesions. IEEE Trans. Med. Imaging, 40, 3728(2021). https://doi.org/10.1109/TMI.2021.3097200

[444] M. Dunlop-Gray et al. Experimental demonstration of an adaptive architecture for direct spectral imaging classification. Opt. Express, 24, 18307(2016). https://doi.org/10.1364/OE.24.018307

[445] C. Hinojosa et al. Spectral-spatial classification from multi-sensor compressive measurements using superpixels, 3413(2019).

[446] C. Hinojosa et al. C-3SPCD: coded aperture similarity constrained design for spatio-spectral classification of single-pixel measurements. Appl. Opt., 61, E21(2022). https://doi.org/10.1364/AO.445326

[447] M. Silva-Maldonado et al. End-to-end compressive spectral classification: a deep learning approach applied to the grading of Tahiti lime, 44(2021).

[448] K. He et al. Deep residual learning for image recognition, 770(2016).

[449] H. Zhang et al. Compressive hyperspectral image classification using a 3D coded convolutional neural network. Opt. Express, 29, 32875(2021). https://doi.org/10.1364/OE.437717

[450] S. Li et al. Deep learning for hyperspectral image classification: an overview. IEEE Trans. Geosci. Remote Sens., 57, 6690(2019). https://doi.org/10.1109/TGRS.2019.2907932

[451] Y. Chen et al. Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geosci. Remote Sens. Lett., 14, 2355(2017). https://doi.org/10.1109/LGRS.2017.2764915

[452] L. Zhu et al. Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens., 56, 5046(2018). https://doi.org/10.1109/TGRS.2018.2805286

[453] B. Liu et al. Supervised deep feature extraction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens., 56, 1909(2017). https://doi.org/10.1109/TGRS.2017.2769673

[454] C. Quintano et al. Spectral unmixing. Int. J. Remote Sens., 33, 5307(2012). https://doi.org/10.1080/01431161.2012.661095

[455] M. Petrou. Mixed pixel classification: an overview, 69(1999).

[456] J. Yu et al. Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery. Int. J. Remote Sens., 38, 773(2017). https://doi.org/10.1080/01431161.2016.1271475

[457] N. Dobigeon et al. A comparison of nonlinear mixing models for vegetated areas using simulated and real hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 7, 1869(2014). https://doi.org/10.1109/JSTARS.2014.2328872

[458] W. Fan et al. Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data. Int. J. Remote Sens., 30, 2951(2009). https://doi.org/10.1080/01431160802558659

[459] Y. Altmann et al. Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery, 1009(2011).

[460] J. Zhang et al. Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches. Int. J. Remote Sens., 22, 615(2001). https://doi.org/10.1080/01431160050505883

[461] B. Schölkopf et al. New support vector algorithms. Neural Comput., 12, 1207(2000). https://doi.org/10.1162/089976600300015565

[462] J. Plaza et al. Nonlinear mixture models for analyzing laboratory simulated-forest hyperspectral data, 5238, 480(2004).

[463] J. S. Bhatt et al. Deep learning in hyperspectral unmixing: a review, 2189(2020).

[464] D. Jin et al. Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 16, 7896(2023). https://doi.org/10.1109/JSTARS.2023.3308037

[465] P. V. Arun et al. Graph neural network based interpretable spectral unmixing for hyperspectral unmixing hyperspectral IIRS data onboard chandrayaan-2 mission, 4202(2023).

[466] H. Hua et al. A dual-stream convolutional feature fusion network for hyperspectral unmixing, 7531(2023).

[467] S. K. Bashetti et al. Self-supervised deep network for automatic target recognition in SAR, 8158(2023).

[468] X. Yuan et al. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Process Mag., 38, 65(2021). https://doi.org/10.1109/MSP.2020.3023869

[469] M. Qiao et al. Deep learning for video compressive sensing. APL Photonics, 5, 3(2020). https://doi.org/10.1063/1.5140721

[470] S. H. Chan et al. Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE Trans. Comput. Imaging, 3, 84(2016). https://doi.org/10.1109/TCI.2016.2629286

[471] J. Ma et al. Deep tensor ADMM-net for snapshot compressive imaging, 10223(2019).

[472] Y. Fu et al. Coded hyperspectral image reconstruction using deep external and internal learning. IEEE Trans. Pattern Anal. Mach. Intell., 44, 3404(2021). https://doi.org/10.1109/TPAMI.2021.3059911

[473] L. Wang et al. Hyperreconnet: joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging. IEEE Trans. Image Process., 28, 2257(2018). https://doi.org/10.1109/TIP.2018.2884076

[474] L. Wang et al. Hyperspectral image reconstruction using a deep spatial-spectral prior, 8032(2019).

[475] L. Wang et al. DNU: deep non-local unrolling for computational spectral imaging, 161(2020).

[476] D. Ulyanov et al. Deep image prior, 9446(2018).

[477] D. S. Jeon et al. Compact snapshot hyperspectral imaging with diffracted rotation(2019).

[478] J. Hauser et al. DD-Net: spectral imaging from a monochromatic dispersed and diffused snapshot. Appl. Opt., 59, 11196(2020). https://doi.org/10.1364/AO.404524

[479] K. Monakhova et al. Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array. Optica, 7, 1298(2020). https://doi.org/10.1364/OPTICA.397214

[480] A. Robles-Kelly. Single image spectral reconstruction for multimedia applications, 251(2015).

[481] S. Galliani et al. Learned spectral super-resolution(2017).

[482] S. Nie et al. Deeply learned filter response functions for hyperspectral reconstruction, 4767(2018).

[483] H. Song et al. Deep-learned broadband encoding stochastic filters for computational spectroscopic instruments. Adv. Theor. Simul., 4, 2000299(2021). https://doi.org/10.1002/adts.202000299

[484] O. Ronneberger et al. U-net: convolutional networks for biomedical image segmentation, 234(2015).

[485] W. Shi et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874(2016).

[486] H. Xu et al. U2Fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell., 44, 502(2020). https://doi.org/10.1109/TPAMI.2020.3012548

[487] D. K. Sahu et al. Different image fusion techniques–a critical review. IJMER, 2, 4298(2012).

[488] Y. Liu et al. A general framework for image fusion based on multi-scale transform and sparse representation. Inform. Fusion, 24, 147(2015). https://doi.org/10.1016/j.inffus.2014.09.004

[489] J. Ma et al. SwinFusion: cross-domain long-range learning for general image fusion via Swin Transformer. IEEE/CAA J. Autom. Sin., 9, 1200(2022). https://doi.org/10.1109/JAS.2022.105686

[490] S. Li et al. Multifocus image fusion using artificial neural networks. Pattern Recogn. Lett., 23, 985(2002). https://doi.org/10.1016/S0167-8655(02)00029-6

[491] Y. Liu et al. “Multi-focus image fusion with a deep convolutional neural network. Inform. Fusion, 36, 191(2017). https://doi.org/10.1016/j.inffus.2016.12.001

[492] X. Guo et al. FuseGAN: learning to fuse multi-focus image via conditional generative adversarial network. IEEE Trans. Multimedia, 21, 1982(2019). https://doi.org/10.1109/TMM.2019.2895292

[493] J. Li et al. DRPL: deep regression pair learning for multi-focus image fusion. IEEE Trans. Image Process., 29, 4816(2020). https://doi.org/10.1109/TIP.2020.2976190

[494] B. Xiao et al. Global-feature encoding U-Net (GEU-Net) for multi-focus image fusion. IEEE Trans. Image Process., 30, 163(2020). https://doi.org/10.1109/TIP.2020.3033158

[495] H. Ma et al. An α-matte boundary defocus model-based cascaded network for multi-focus image fusion. IEEE Trans. Image Process., 29, 8668(2020). https://doi.org/10.1109/TIP.2020.3018261

[496] H. Zhang et al. MSTCNN: A transformer with multi-scale convolutional neural network for multi-focus image fusion(2023). https://doi.org/10.21203/rs.3.rs-3303519/v1

[497] H. Li et al. Multi-focus image fusion using u-shaped networks with a hybrid objective. IEEE Sensors J., 19, 9755(2019). https://doi.org/10.1109/JSEN.2019.2928818

[498] X. Yan et al. Structural similarity loss for learning to fuse multi-focus images. Sensors, 20, 6647(2020). https://doi.org/10.3390/s20226647

[499] J. Huang et al. A generative adversarial network with adaptive constraints for multi-focus image fusion. Neural Comput. Appl., 32, 15119(2020). https://doi.org/10.1007/s00521-020-04863-1

[500] S. Li et al. Image fusion with guided filtering. IEEE Trans. Image Process., 22, 2864(2013). https://doi.org/10.1109/TIP.2013.2244222

[501] X. Qiu et al. Guided filter-based multi-focus image fusion through focus region detection. Signal Process. Image Commun., 72, 35(2019). http://10.1016/j.image.2018.12.004

[502] H. Li et al. Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection. Optik, 124, 40(2013). https://doi.org/10.1016/j.ijleo.2011.11.088

[503] Y. Liu et al. Multi-focus image fusion with dense SIFT. Inform. Fusion, 23, 139(2015). https://doi.org/10.1016/j.inffus.2014.05.004

[504] X. Jin et al. Adversarial attacks on multi-focus image fusion models. Comput. Secur., 134, 103455(2023). https://doi.org/10.1016/j.cose.2023.103455

[505] J. Li et al. Multi-focus image fusion based on multiscale fuzzy quality assessment. Digit. Signal Process., 153, 104592(2024). https://doi.org/10.1016/j.dsp.2024.104592

[506] J. Zhang et al. Exploit the best of both end-to-end and map-based methods for multi-focus image fusion. IEEE Trans. Multimedia, 26, 6411(2024). https://doi.org/10.1109/TMM.2024.3350924

[507] H. Xu et al. MEF-GAN: Multi-exposure image fusion via generative adversarial networks. IEEE Trans. Image Process., 29, 7203(2020). https://doi.org/10.1109/TIP.2020.2999855

[508] N. K. Kalantari et al. Deep high dynamic range imaging of dynamic scenes. ACM Trans. Graph., 36, 1(2017). https://doi.org/10.1145/3072959.3073609

[509] J. Wang et al. End-to-end exposure fusion using convolutional neural network. IEICE Trans. Inf. Syst., E101.D, 560(2018). https://doi.org/10.1587/transinf.2017EDL8173

[510] Z. Pan et al. Multi-exposure high dynamic range imaging with informative content enhanced network. Neurocomputing, 386, 147(2020). https://doi.org/10.1016/j.neucom.2019.12.093

[511] X. Deng et al. Deep coupled feedback network for joint exposure fusion and image super-resolution. IEEE Trans. Image Process., 30, 3098(2021). https://doi.org/10.1109/TIP.2021.3058764

[512] K. Ram Prabhakar et al. Deepfuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs, 4714(2017).

[513] H. Xu et al. MEF-GAN: multi-exposure image fusion via generative adversarial networks. IEEE Trans. Image Process., 29, 7203(2020). https://doi.org/10.1109/TIP.2020.2999855

[514] J. Cai et al. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans. Image Process., 27, 2049(2018). https://doi.org/10.1109/TIP.2018.2794218

[515] Z. Yang et al. GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks. Neural Comput. Appl., 33, 6133(2021). https://doi.org/10.1007/s00521-020-05387-4

[516] F. Xu et al. Multi-exposure image fusion techniques: a comprehensive review. Remote Sens., 14, 771(2022). https://doi.org/10.3390/rs14030771

[517] H. Zhang et al. SDNet: a versatile squeeze-and-decomposition network for real-time image fusion. Int. J. Comput. Vis., 129, 2761(2021). https://doi.org/10.1007/s11263-021-01501-8

[518] L. Jian et al. SEDRFuse: a symmetric encoder–decoder with residual block network for infrared and visible image fusion. IEEE Trans. Instrum. Meas., 70, 1(2020). https://doi.org/10.1109/TIM.2020.3022438

[519] J. Xu et al. An infrared and visible image fusion network based on multi‐scale feature cascades and non‐local attention. IET Image Process., 18, 8(2024). https://doi.org/10.1049/ipr2.13088

[520] L. Tang et al. DIVFusion: darkness-free infrared and visible image fusion. Inform. Fusion, 91, 477(2023). https://doi.org/10.1016/j.inffus.2022.10.034

[521] Y. Li et al. Unsupervised densely attention network for infrared and visible image fusion. Multimedia Tools Appl., 79, 34685(2020). https://doi.org/10.1007/s11042-020-09301-x

[522] Y. Zhou et al. A multi-weight fusion framework for infrared and visible image fusion. Multimedia Tools Appl., 83, 68931(2024). https://doi.org/10.1007/s11042-024-18141-y

[523] J. Ma et al. FusionGAN: a generative adversarial network for infrared and visible image fusion. Inform. Fusion, 48, 11(2019). https://doi.org/10.1016/j.inffus.2018.09.004

[524] J. Ma et al. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process., 29, 4980(2020). https://doi.org/10.1109/TIP.2020.2977573

[525] J. Wu et al. GAN-GA: infrared and visible image fusion generative adversarial network based on global awareness. Appl. Intell., 54, 7296(2024). https://doi.org/10.1007/s10489-024-05561-4

[526] J. Du et al. An overview of multi-modal medical image fusion. Neurocomputing, 215, 3(2016). https://doi.org/10.1016/j.neucom.2015.07.160

[527] A. P. James et al. Medical image fusion: a survey of the state of the art. Inform. Fusion, 19, 4(2014). https://doi.org/10.1016/j.inffus.2013.12.002

[528] Y. Liu et al. A medical image fusion method based on convolutional neural networks, 1(2017).

[529] J. Fu et al. “A multiscale residual pyramid attention network for medical image fusion. Biomed. Signal Process. Control, 66, 102488(2021). https://doi.org/10.1016/j.bspc.2021.102488

[530] O. C. Do et al. An efficient approach to medical image fusion based on optimization and transfer learning with VGG19. Biomed. Signal Process. Control, 87, 105370(2024). https://doi.org/10.1016/j.bspc.2023.105370

[531] N. Liang et al. Medical image fusion with deep neural networks. Sci. Rep., 14, 7972(2024). https://doi.org/10.1038/s41598-024-58665-9

[532] Y. Zhou et al. Multimodal medical image fusion network based on target information enhancement. IEEE Access, 12, 70851(2024).

[533] T. Zhou et al. Deep learning methods for medical image fusion: A review. Comput. Biol. Med., 160, 106959(2023). https://doi.org/10.1016/j.compbiomed.2023.106959

[534] Y. Wei et al. Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geosci. Remote Sens. Lett., 14, 1795(2017). https://doi.org/10.1109/LGRS.2017.2736020

[535] G. Masi et al. Pansharpening by convolutional neural networks. Remote Sens., 8, 594(2016). https://doi.org/10.3390/rs8070594

[536] J. Cai et al. Super-resolution-guided progressive pansharpening based on a deep convolutional neural network. IEEE Trans. Geosci. Remote Sens., 59, 5206(2020). https://doi.org/10.1109/TGRS.2020.3015878

[537] A. Guo et al. Unsupervised blur kernel learning for pansharpening, 633(2020).

[538] Q. Liu et al. Supervised-unsupervised combined deep convolutional neural networks for high-fidelity pansharpening. Inform. Fusion, 89, 292(2023). https://doi.org/10.1016/j.inffus.2022.08.018

[539] Q. Liu et al. PSGAN: a generative adversarial network for remote sensing image pan-sharpening. IEEE Trans. Geosci. Remote Sens., 59, 10227(2020). https://doi.org/10.1109/TGRS.2020.3042974

[540] Y. Wu et al. Pansharpening using unsupervised generative adversarial networks with recursive mixed-scale feature fusion. IEEE J. Sel. Topics Appl. Earth Observ. Rem. Sens., 16, 3742(2023).

[541] J. Ma et al. Pan-GAN: an unsupervised pan-sharpening method for remote sensing image fusion. Inform. Fusion, 62, 110(2020). https://doi.org/10.1016/j.inffus.2020.04.006

[542] L. He et al. Unsupervised pansharpening based on double-cycle consistency(2024).

[543] L. Fan et al. Brief review of image denoising techniques. Vis. Comput. Ind, Biomed., 2, 7(2019). https://doi.org/10.1186/s42492-019-0016-7

[544] S. Izadi et al. Image denoising in the deep learning era. Artif. Intell. Rev., 56, 5929(2023). https://doi.org/10.1007/s10462-022-10305-2

[545] X. Mao et al. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections, 26(2016).

[546] T. Wang et al. Dilated deep residual network for image denoising, 1272(2017).

[547] J. Guo et al. Toward convolutional blind denoising of real photographs, 1712(2019).

[548] C. Tian et al. Image denoising using deep CNN with batch renormalization. Neural Netw., 121, 461(2020). https://doi.org/10.1016/j.neunet.2019.08.022

[549] S. Rezvani et al. Single image denoising via a new lightweight learning-based model. IEEE Access, 12, 121077(2024). https://doi.org/10.1109/ACCESS.2024.3450842

[550] W. Wu et al. Dual residual attention network for image denoising. Pattern Recogn., 149, 110291(2024). https://doi.org/10.1016/j.patcog.2024.110291

[551] J. Chen et al. Image blind denoising with generative adversarial network based noise modeling, 3155(2018).

[552] Y. Wang et al. RCA-GAN: an improved image denoising algorithm based on generative adversarial networks. Electronics, 12, 4595(2023). https://doi.org/10.3390/electronics12224595

[553] V. Lempitsky et al. Deep image prior, 9946(2018).

[554] G. Mataev et al. DeepRED: deep image prior powered by RED(2019).

[555] Y. Wang et al. Hyperspectral denoising using asymmetric noise modeling deep image prior. Remote Sens., 15, 1970(2023). https://doi.org/10.3390/rs15081970

[556] J. Lehtinen et al. Noise2Noise: learning image restoration without clean data(2018).

[557] S. Laine et al. High-quality self-supervised deep image denoising, 32(2019).

[558] S. Soltanayev et al. Training deep learning based denoisers without ground truth data, 31(2018).

[559] M. Zhussip et al. Extending Stein’s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images, 32(2019).

[560] Y. Zhang et al. Kindling the darkness: a practical low-light image enhancer, 1632(2019).

[561] Y. Xiang et al. WMANet: Wavelet-based multi-scale attention network for low-light image enhancement. IEEE Access, 12, 105674(2024).

[562] S. Yang et al. Rethinking low-light enhancement via transformer-GAN. IEEE Signal Process Lett., 29, 1082(2022). https://doi.org/10.1109/LSP.2022.3167331

[563] Y. Liu et al. PD-GAN: perceptual-details gan for extremely noisy low light image enhancement, 1840(2021).

[564] W. Han et al. UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement. IET Image Process., 18, 2154(2024). https://doi.org/10.1049/ipr2.13092

[565] M. Gharbi et al. Deep bilateral learning for real-time image enhancement. ACM TOG, 36, 1(2017). https://doi.org/10.1145/3072959.3073592

[566] F. Lv et al. MBLLEN: low-light image/video enhancement using CNNS. BMVC, 220, 4(2018).

[567] J. Perez et al. A deep learning approach for underwater image enhancement, 183(2017).

[568] X. Cao et al. NUICNet: non-uniform illumination correction for underwater image using fully convolutional network. IEEE Access, 8, 109989(2020). https://doi.org/10.1109/ACCESS.2020.3002593

[569] J. Li et al. WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Rob. Autom. Lett., 3, 387(2017).

[570] N. Wang et al. UWGAN: underwater GAN for real-world underwater color restoration and dehazing(2019).

[571] Q. Liu et al. WSDS-GAN: a weak-strong dual supervised learning method for underwater image enhancement. Pattern Recogn., 143, 109774(2023). https://doi.org/10.1016/j.patcog.2023.109774

[572] B. Li et al. An all-in-one network for dehazing and beyond(2017).

[573] W. Ren et al. Gated fusion network for single image dehazing, 3253(2018).

[574] L. Li et al. Semi-supervised image dehazing. IEEE Trans. Image Process., 29, 2766(2019). https://doi.org/10.1109/TIP.2019.2952690

[575] J. Park et al. Fusion of heterogeneous adversarial networks for single image dehazing. IEEE Trans. Image Process., 29, 4721(2020). https://doi.org/10.1109/TIP.2020.2975986

[576] C. Dong et al. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell., 38, 295(2015). https://doi.org/10.1109/TPAMI.2015.2439281

[577] C. Ledig et al. Photo-realistic single image super-resolution using a generative adversarial network, 4681(2017).

[578] Y. Chen et al. Image super-resolution reconstruction based on feature map attention mechanism. Appl. Intell., 51, 4367(2021). https://doi.org/10.1007/s10489-020-02116-1

[579] J. Tu et al. SWCGAN: generative adversarial network combining swin transformer and CNN for remote sensing image super-resolution. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 15, 5662(2022). https://doi.org/10.1109/JSTARS.2022.3190322

[580] H. Shen et al. Lossless compression of curated erythrocyte images using deep autoencoders for malaria infection diagnosis, 1(2016).

[581] F. Mentzer et al. Practical full resolution learned lossless image compression, 10629(2019).

[582] H. Rhee et al. LC-FDNet: learned lossless image compression with frequency decomposition network, 6033(2022).

[583] Q. Min et al. Lossless medical image compression based on anatomical information and deep neural networks. Biomed. Signal Proces., 74, 103499(2022). https://doi.org/10.1016/j.bspc.2022.103499

[584] W. Li et al. Deep image compression with residual learning. Appl. Sci., 10, 4023(2020). https://doi.org/10.3390/app10114023

[585] Z. Cheng et al. Learned image compression with discretized gaussian mixture likelihoods and attention modules, 7939(2020).

[586] T. Chen et al. End-to-end learnt image compression via non-local attention optimization and improved context modeling. IEEE Trans. Image Process., 30, 3179(2021). https://doi.org/10.1109/TIP.2021.3058615

[587] A. Tawfik et al. A generic real time autoencoder-based lossy image compression, 1(2022).

微信扫一扫：分享

微信扫一扫：分享