[1] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018). https://doi.org/10.1038/s41566-018-0253-x

[2] L. Waller, G. Situ, J. W. Fleischer. Phase-space measurement and coherence synthesis of optical beams. Nat. Photonics, 6, 474-479(2012). https://doi.org/10.1038/nphoton.2012.144

[3] J. C. Ramella-Roman, I. Saytashev, M. Piccini. A review of polarization-based imaging technologies for clinical and preclinical applications. J. Opt., 22, 123001(2020). https://doi.org/10.1088/2040-8986/abbf8a

[4] N. Antipa et al. DiffuserCAM: lensless single-exposure 3D imaging. Optica, 5, 1-9(2018). https://doi.org/10.1364/OPTICA.5.000001

[5] A. Wagadarikar et al. Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt., 47, B44-B51(2008). https://doi.org/10.1364/AO.47.000B44

[6] L. Gao et al. Single-shot compressed ultrafast photography at one hundred billion frames per second. Nature, 516, 74-77(2014). https://doi.org/10.1038/nature14005

[7] M. Piccardo et al. Vortex laser arrays with topological charge control and self-healing of defects. Nat. Photonics, 16, 359-365(2022). https://doi.org/10.1038/s41566-022-00986-0

[8] U. Leonhardt. Measuring the Quantum State of Light, 22(1997).

[9] M. G. L. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc., 198, 82-87(2000). https://doi.org/10.1046/j.1365-2818.2000.00710.x

[10] G. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 7, 739-745(2013). https://doi.org/10.1038/nphoton.2013.187

[11] H. Wang et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 16, 103-110(2019). https://doi.org/10.1038/s41592-018-0239-0

[12] J.-H. Park et al. Large-field-of-view imaging by multi-pupil adaptive optics. Nat. Methods, 14, 581-583(2017). https://doi.org/10.1038/nmeth.4290

[13] J. Lich et al. Single-shot 3D incoherent imaging with diffuser endoscopy. Light: Adv. Manuf., 5, 987(2024). https://doi.org/10.3390/photonics10090987

[14] M. Liao et al. Extending the depth-of-field of imaging systems with a scattering diffuser. Sci. Rep., 9, 7165(2019). https://doi.org/10.1038/s41598-019-43593-w

[15] V. Boominathan et al. Recent advances in lensless imaging. Optica, 9, 1-16(2022). https://doi.org/10.1364/OPTICA.431361

[16] P. A. Morris et al. Imaging with a small number of photons. Nat. Commun., 6, 5913(2015). https://doi.org/10.1038/ncomms6913

[17] Z. Li et al. Single-photon imaging over 200 km. Optica, 8, 344-349(2021). https://doi.org/10.1364/OPTICA.408657

[18] J. Bertolotti et al. Non-invasive imaging through opaque scattering layers. Nature, 491, 232-234(2012). https://doi.org/10.1038/nature11578

[19] M. O’Toole, D. B. Lindell, G. Wetzstein. Confocal non-line-of-sight imaging based on the light-cone transform. Nature, 555, 338-341(2018). https://doi.org/10.1038/nature25489

[20] F. Willomitzer et al. Fast non-line-of-sight imaging with high-resolution and wide field of view using synthetic wavelength holography. Nat. Commun., 12, 6647(2021). https://doi.org/10.1038/s41467-021-26776-w

[21] T. Wang et al. Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis. Neurophotonics, 11, S11505(2024). https://doi.org/10.1117/1.NPh.11.S1.S11505

[22] W. C. Karl et al. The foundations of computational imaging: a signal processing perspective. IEEE Signal Process. Mag., 40, 40-53(2023). https://doi.org/10.1109/MSP.2023.3274328

[23] M. Bertero, P. Boccacci. Introduction to Inverse Problems in Imaging(1998).

[24] G. Barbastathis, A. Ozcan, G. Situ. On the use of deep learning for computational imaging. Optica, 6, 921-943(2019). https://doi.org/10.1364/OPTICA.6.000921

[25] J. Rosen et al. Roadmap on computational methods in optical imaging and holography. Appl. Phys. B, 130, 166(2024). https://doi.org/10.1007/s00340-024-08280-3

[26] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015). https://doi.org/10.1038/nature14539

[27] G. Ongie et al. Deep learning techniques for inverse problems in imaging. IEEE J. Sel. Areas Inform. Theory, 1, 39-56(2020). https://doi.org/10.1109/JSAIT.2020.2991563

[28] A. Lucas et al. Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Process. Mag., 35, 20-36(2018). https://doi.org/10.1109/MSP.2017.2760358

[29] G. Situ. Deep holography. Light: Adv. Manuf., 3, 278(2022). https://doi.org/10.37188/lam.2022.013

[30] C. Zuo et al. Deep learning in optical metrology: a review. Light: Sci. Appl., 11, 39(2022). https://doi.org/10.1038/s41377-022-00714-x

[31] K. Wang et al. On the use of deep learning for phase recovery. Light: Sci. Appl., 13, 4(2024). https://doi.org/10.1038/s41377-023-01340-x

[32] L. Huang et al. Spectral imaging with deep learning. Light: Sci. Appl., 11, 61(2022). https://doi.org/10.1038/s41377-022-00743-6

[33] C. Moodley, A. Forbes. Advances in quantum imaging with machine intelligence. Laser Photonics Rev., 18, 2300939(2024). https://doi.org/10.1002/lpor.202300939

[34] X. Yuan, D. J. Brady, A. K. Katsaggelos. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Process. Mag., 38, 65-88(2021). https://doi.org/10.1109/MSP.2020.3023869

[35] K. Song et al. Advances and challenges of single-pixel imaging based on deep learning. Laser Photonics Rev., 19, 2401397(2024). https://doi.org/10.1002/lpor.202401397

[36] C. Zhang et al. Understanding deep learning (still) requires rethinking generalization. Commun. ACM, 64, 107-115(2021). https://doi.org/10.1145/3446776

[37] G. Marcus. Deep learning: a critical appraisal(2018).

[38] I. Goodfellow, Y. Bengio, A. Courville. Deep Learning(2016).

[39] J. N. Mait, G. W. Euliss, R. A. Athale. Computational imaging. Adv. Opt. Photonics, 10, 409-483(2018). https://doi.org/10.1364/AOP.10.000409

[40] Y. J. Chul, H. Yoseob, C. Eunju. Deep convolutional framelets: a general deep learning framework for inverse problems. SIAM J. Imaging Sci., 11, 991-1048(2018). https://doi.org/10.1137/17M1141771

[41] W. Liu et al. A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26(2017). https://doi.org/10.1016/j.neucom.2016.12.038

[42] M. Z. Alom et al. A state-of-the-art survey on deep learning theory and architectures. Electronics, 8, 292(2019). https://doi.org/10.3390/electronics8030292

[43] M. Raissi, P. Perdikaris, G. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378, 686-707(2019). https://doi.org/10.1016/j.jcp.2018.10.045

[44] J. Lim, D. Psaltis. MaxwellNet: physics-driven deep neural network training based on Maxwell’s equations. APL Photonics, 7, 011301(2022). https://doi.org/10.1063/5.0071616

[45] R. Iten et al. Discovering physical concepts with neural networks. Phys. Rev. Lett., 124, 010508(2020). https://doi.org/10.1103/PhysRevLett.124.010508

[46] C. Rao et al. Encoding physics to learn reaction–diffusion processes. Nat. Mach. Intell., 5, 765-779(2023). https://doi.org/10.1038/s42256-023-00685-7

[47] R. C. Gonzalez. Digital Image Processing(2009).

[48] A. N. Tikhonov. On the regularization of ill-posed problems. Dokl. Akad. Nauk SSSR, 153, 49-52(1963).

[49] D. L. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, 52, 1289-1306(2006). https://doi.org/10.1109/TIT.2006.871582

[50] L. I. Rudin, S. Osher, E. Fatemi. Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom., 60, 259-268(1992). https://doi.org/10.1016/0167-2789(92)90242-F

[51] M. F. Duarte et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag., 25, 83-91(2008). https://doi.org/10.1109/MSP.2007.914730

[52] D. J. Brady. Optical Imaging and Spectroscopy(2009).

[53] Y. Rivenson, A. Stern, B. Javidi. Overview of compressive sensing techniques applied in holography. Appl. Opt., 52, A423-A432(2013). https://doi.org/10.1364/AO.52.00A423

[54] W. Zhang et al. Twin-image-free holography: a compressive sensing approach. Phys. Rev. Lett., 121, 093902(2018). https://doi.org/10.1103/PhysRevLett.121.093902

[55] Y. Gao, L. Cao. Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography. Light: Adv. Manuf., 4, 37-53(2023). https://doi.org/10.37188/lam.2023.006

[56] J. Wu et al. Single-shot lensless imaging with Fresnel zone aperture and incoherent illumination. Light: Sci. Appl., 9, 53(2020). https://doi.org/10.1038/s41377-020-0289-9

[57] W. Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol., 40, 606-617(2022). https://doi.org/10.1038/s41587-021-01092-2

[58] K. Hornik, M. Stinchcombe, H. White. Multilayer feedforward networks are universal approximators. Neural Netw., 2, 359-366(1989). https://doi.org/10.1016/0893-6080(89)90020-8

[59] S. Ruder. An overview of gradient descent optimization algorithms(2017).

[60] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning representations by back-propagating errors. Nature, 323, 533-536(1986). https://doi.org/10.1038/323533a0

[61] J. Schmidhuber. Deep learning in neural networks: an overview. Neural Netw., 61, 85-117(2015). https://doi.org/10.1016/j.neunet.2014.09.003

[62] Y. LeCun et al. Backpropagation applied to handwritten zip code recognition. Neural Comput., 1, 541-551(1989). https://doi.org/10.1162/neco.1989.1.4.541

[63] S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Comput., 9, 1735-1780(1997). https://doi.org/10.1162/neco.1997.9.8.1735

[64] I. Guyon, A. Vaswani et al. Attention is all you need, 30(2017).

[65] J. J. Hopfield, D. W. Tank. ‘Neural’ computation of decisions in optimization problems. Biol. Cybern., 52, 141-152(1985). https://doi.org/10.1007/BF00339943

[66] G. Situ. Photonics and AI: a conversation with professor Demetri Psaltis. Adv. Photonics, 6, 050501(2024). https://doi.org/10.1117/1.AP.6.5.050501

[67] D. Psaltis, N. Farhat. Optical information processing based on an associative-memory model of neural nets with thresholding and feedback. Opt. Lett., 10, 98-100(1985). https://doi.org/10.1364/OL.10.000098

[68] M. Takeda, J. W. Goodman. Neural networks for computation: number representations and programming complexity. Appl. Opt., 25, 3033-3046(1986). https://doi.org/10.1364/AO.25.003033

[69] Y.-T. Zhou et al. Image restoration using a neural network. IEEE Trans. Acoust. Speech Signal Process., 36, 1141-1151(1988). https://doi.org/10.1109/29.1641

[70] D. Just, D. T. Ling. Neural networks for binarizing computer-generated holograms. Opt. Commun., 81, 1-5(1991). https://doi.org/10.1016/0030-4018(91)90282-I

[71] W. Jüptner, M. Takeda, K. Nagatome, W. Osten, Y. Watanabe. Phase unwraping by neural network, 136-141(1993).

[72] T. Kreis, W. Jüptner, R. Biedermann. Neural network approach to holographic nondestructive testing. Appl. Opt., 34, 1407-1415(1995). https://doi.org/10.1364/AO.34.001407

[73] Y. Frauel, B. Javidi. Neural network for three-dimensional object recognition based on digital holography. Opt. Lett., 26, 1478-1480(2001). https://doi.org/10.1364/OL.26.001478

[74] G. Wetzstein et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020). https://doi.org/10.1038/s41586-020-2973-6

[75] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks, 1097-1105(2012).

[76] D. Silver et al. Mastering the game of go with deep neural networks and tree search. Nature, 529, 484-489(2016). https://doi.org/10.1038/nature16961

[77] L. Tian, L. Waller. 3D intensity and phase imaging from light field measurements in an LED array microscope. Optica, 2, 104-111(2015). https://doi.org/10.1364/OPTICA.2.000104

[78] U. S. Kamilov et al. Learning approach to optical tomography. Optica, 2, 517-522(2015). https://doi.org/10.1364/OPTICA.2.000517

[79] A. Sinha et al. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017). https://doi.org/10.1364/OPTICA.4.001117

[80] Y. Rivenson et al. Deep learning microscopy. Optica, 4, 1437-1443(2017). https://doi.org/10.1364/OPTICA.4.001437

[81] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light: Sci. Appl., 7, 17141(2018). https://doi.org/10.1038/lsa.2017.141

[82] M. Lyu et al. Deep-learning-based ghost imaging. Sci. Rep., 7, 17865(2017). https://doi.org/10.1038/s41598-017-18171-7

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

[84] X. Luo et al. Revolutionizing optical imaging: computational imaging via deep learning. Photonics Insights, 4, R03(2025). https://doi.org/10.3788/PI.2025.R03

[85] Y. Rivenson, Y. Wu, A. Ozcan. Deep learning in holography and coherent imaging. Light: Sci. Appl., 8, 85(2019). https://doi.org/10.1038/s41377-019-0196-0

[86] H. Wang, M. Lyu, G. Situ. EholoNet: a learning-based end-to-end approach for in-line digital holographic reconstruction. Opt. Express, 26, 22603-22614(2018). https://doi.org/10.1364/OE.26.022603

[87] K. Wang et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. Opt. Lett., 44, 4765-4768(2019). https://doi.org/10.1364/OL.44.004765

[88] T. Nguyen et al. Deep learning approach for Fourier ptychography microscopy. Opt. Express, 26, 26470-26484(2018). https://doi.org/10.1364/OE.26.026470

[89] Y. Xue et al. Reliable deep-learning-based phase imaging with uncertainty quantification. Optica, 6, 618-629(2019). https://doi.org/10.1364/OPTICA.6.000618

[90] 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

[91] C. F. Higham et al. Deep learning for real-time single-pixel video. Sci. Rep., 8, 2369(2018). https://doi.org/10.1038/s41598-018-20521-y

[92] Y. He et al. Ghost imaging based on deep learning. Sci. Rep., 8, 6469(2018). https://doi.org/10.1038/s41598-018-24731-2

[93] F. Wang et al. Learning from simulation: an end-to-end deep-learning approach for computational ghost imaging. Opt. Express, 27, 25560-25572(2019). https://doi.org/10.1364/OE.27.025560

[94] F. Wang et al. Far-field super-resolution ghost imaging with a deep neural network constraint. Light: Sci. Appl., 11, 1(2022). https://doi.org/10.1038/s41377-021-00680-w

[95] F. Wang et al. Single-pixel imaging using physics enhanced deep learning. Photonics Res., 10, 104-110(2022). https://doi.org/10.1364/PRJ.440123

[96] E. Nehme et al. Deep-storm: super-resolution single-molecule microscopy by deep learning. Optica, 5, 458-464(2018). https://doi.org/10.1364/OPTICA.5.000458

[97] M. Weigert et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods, 15, 1090-1097(2018). https://doi.org/10.1038/s41592-018-0216-7

[98] Y. Wu et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat. Methods, 16, 1323-1331(2019). https://doi.org/10.1038/s41592-019-0622-5

[99] C. Qiao et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods, 18, 194-202(2021). https://doi.org/10.1038/s41592-020-01048-5

[100] X. Chen et al. Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging. PhotoniX, 5, 4(2024). https://doi.org/10.1186/s43074-024-00121-y

[101] L. Qu et al. Self-inspired learning for denoising live-cell super-resolution microscopy. Nat. Methods, 21, 1895-1908(2024). https://doi.org/10.1038/s41592-024-02400-9

[102] Z. Wen et al. Single multimode fibre for in vivo light-field-encoded endoscopic imaging. Nat. Photonics, 17, 679-687(2023). https://doi.org/10.1038/s41566-023-01240-x

[103] S. Li et al. Imaging through glass diffusers using densely connected convolutional networks. Optica, 5, 803-813(2018). https://doi.org/10.1364/OPTICA.5.000803

[104] Y. Li, Y. Xue, L. Tian. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181-1190(2018). https://doi.org/10.1364/OPTICA.5.001181

[105] M. Lyu et al. Learning-based lensless imaging through optically thick scattering media. Adv. Photonics, 1, 036002(2019). https://doi.org/10.1117/1.AP.1.3.036002

[106] Y. Sun et al. Image reconstruction through dynamic scattering media based on deep learning. Opt. Express, 27, 16032-16046(2019). https://doi.org/10.1364/OE.27.016032

[107] S. Zheng et al. Incoherent imaging through highly nonstatic and optically thick turbid media based on neural network. Photonics Res., 9, B220-B228(2021). https://doi.org/10.1364/PRJ.416246

[108] Z. Fu et al. Adaptive imaging through dense dynamic scattering media using transfer learning. Opt. Express, 32, 13688-13700(2024). https://doi.org/10.1364/OE.519771

[109] S. Zhu et al. Imaging through unknown scattering media based on physics-informed learning. Photonics Res., 9, B210-B219(2021). https://doi.org/10.1364/PRJ.416551

[110] Z. Wang et al. Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning. Nat. Methods, 18, 551-556(2021). https://doi.org/10.1038/s41592-021-01058-x

[111] N. Wagner et al. Deep learning-enhanced light-field imaging with continuous validation. Nat. Methods, 18, 557-563(2021). https://doi.org/10.1038/s41592-021-01136-0

[112] Y. Xue et al. Deep-learning-augmented computational miniature mesoscope. Optica, 9, 1009-1021(2022). https://doi.org/10.1364/OPTICA.464700

[113] K. Monakhova et al. Learned reconstructions for practical mask-based lensless imaging. Opt. Express, 27, 28075-28090(2019). https://doi.org/10.1364/OE.27.028075

[114] V. Boominathan et al. PhlatCAM: designed phase-mask based thin lensless camera. IEEE Trans. Pattern Anal. Mach. Intell., 42, 1618-1629(2020). https://doi.org/10.1109/TPAMI.2020.2987489

[115] K. Monakhova et al. Untrained networks for compressive lensless photography. Opt. Express, 29, 20913-20929(2021). https://doi.org/10.1364/OE.424075

[116] J. Wu, L. Cao, G. Barbastathis. DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging. Opt. Lett., 46, 130-133(2021). https://doi.org/10.1364/OL.411228

[117] M. Araya-Polo et al. Deep-learning tomography. Lead. Edge, 37, 58-66(2018). https://doi.org/10.1190/tle37010058.1

[118] G. Wang, J. C. Ye, B. D. Man. Deep learning for tomographic image reconstruction. Nat. Mach. Intell., 2, 737-748(2020). https://doi.org/10.1038/s42256-020-00273-z

[119] J. Yoo et al. Deep learning diffuse optical tomography. IEEE Trans. Med. Imaging, 39, 877-887(2020). https://doi.org/10.1109/TMI.2019.2936522

[120] Y. Jin et al. Three-dimensional rapid flame chemiluminescence tomography via deep learning. Opt. Express, 27, 27308-27334(2019). https://doi.org/10.1364/OE.27.027308

[121] A. Goy et al. High-resolution limited-angle phase tomography of dense layered objects using deep neural networks. Proc. Natl. Acad. Sci., 116, 19848-19856(2019). https://doi.org/10.1073/pnas.1821378116

[122] J. Sun et al. AI-driven projection tomography with multicore fibre-optic cell rotation. Nat. Commun., 15, 147(2024). https://doi.org/10.1038/s41467-023-44280-1

[123] A. Ozdemir, K. Polat. Deep learning applications for hyperspectral imaging: a systematic review. Complex Intell. Syst., 9, 2713-2745(2021).

[124] S. Zheng et al. Deep plug-and-play priors for spectral snapshot compressive imaging. Photonics Res., 9, B18-B29(2021). https://doi.org/10.1364/PRJ.411745

[125] L. Wang et al. Snapshot spectral compressive imaging reconstruction using convolution and contextual transformer. Photonics Res., 10, 1848-1858(2022). https://doi.org/10.1364/PRJ.458231

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

[127] J. Zhang et al. Phase unwrapping in optical metrology via denoised and convolutional segmentation networks. Opt. Express, 27, 14903-14912(2019). https://doi.org/10.1364/OE.27.014903

[128] S. Feng et al. Deep-learning-based fringe-pattern analysis with uncertainty estimation. Optica, 8, 1507-1510(2021). https://doi.org/10.1364/OPTICA.434311

[129] A. G. Marrugo, F. Gao, S. Zhang. State-of-the-art active optical techniques for three-dimensional surface metrology: a review. J. Opt. Soc. Am. A, 37, B60-B77(2020). https://doi.org/10.1364/JOSAA.398644

[130] J. Deng et al. ImageNet: a large-scale hierarchical image database, 248-255(2009). https://doi.org/10.1109/CVPR.2009.5206848

[131] H. Liu et al. Learning-based real-time imaging through dynamic scattering media. Light: Sci. Appl., 13, 194(2024). https://doi.org/10.1038/s41377-024-01569-0

[132] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015). https://doi.org/10.1007/978-3-319-24574-4_28

[133] I. J. Goodfellow et al. Generative adversarial nets, 2672-2680(2014).

[134] J. Johnson, A. Alahi, F.-F. Li. Perceptual losses for real-time style transfer and super-resolution. Lect. Notes Comput. Sci., 9906, 694-711(2016). https://doi.org/10.1007/978-3-319-46475-6_43

[135] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

[136] K. A. S. H. Kulathilake et al. A review on deep learning approaches for low-dose computed tomography restoration. Complex Intell. Syst., 9, 2713-2745(2023). https://doi.org/10.1007/s40747-021-00405-x

[137] A. Pan, C. Zuo, B. Yao. High-resolution and large field-of-view Fourier ptychographic microscopy and its applications in biomedicine. Rep. Progr. Phys., 83, 096101(2020). https://doi.org/10.1088/1361-6633/aba6f0

[138] Y. Bian et al. Deep learning colorful ptychographic iterative engine lens-less diffraction microscopy. Opt. Lasers Eng., 150, 106843(2022). https://doi.org/10.1016/j.optlaseng.2021.106843

[139] J. Sun et al. Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning. Opt. Lett., 49, 342-345(2024). https://doi.org/10.1364/OL.509772

[140] 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

[141] L. Kreiss et al. Digital staining in optical microscopy using deep learning—a review. PhotoniX, 4, 34(2023). https://doi.org/10.1186/s43074-023-00113-4

[142] E. Tseng et al. Neural nano-optics for high-quality thin lens imaging. Nat. Commun., 12, 6493(2021). https://doi.org/10.1038/s41467-021-26443-0

[143] J. Chang, G. Wetzstein. Deep optics for monocular depth estimation and 3D object detection, 10193-10202(2019). https://doi.org/10.1109/ICCV.2019.01029

[144] J. Wu et al. Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis. Sci. Rep., 12, 18846(2022). https://doi.org/10.1038/s41598-022-23490-5

[145] J. Bertolotti, O. Katz. Imaging in complex media. Nat. Phys., 18, 1008-1017(2022). https://doi.org/10.1038/s41567-022-01723-8

[146] W. Tahir, H. Wang, L. Tian. Adaptive 3D descattering with a dynamic synthesis network. Light: Sci. Appl., 11, 42(2022). https://doi.org/10.1038/s41377-022-00730-x

[147] 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

[148] Y. An et al. Learning to decompose the modes in few-mode fibers with deep convolutional neural network. Opt. Express, 27, 10127-10137(2019). https://doi.org/10.1364/OE.27.010127

[149] Q. Zhang et al. Learning the matrix of few-mode fibers for high-fidelity spatial mode transmission. APL Photonics, 7, 066104(2022). https://doi.org/10.1063/5.0088605

[150] S. Rothe et al. Intensity-only mode decomposition on multimode fibers using a densely connected convolutional network. J. Lightwave Technol., 39, 1672-1679(2021). https://doi.org/10.1109/JLT.2020.3041374

[151] Q. Zhang et al. Reference-less decomposition of highly multimode fibers using a physics-driven neural network(2024).

[152] D. Jin et al. Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning. Nat. Mach. Intell., 3, 876-884(2021). https://doi.org/10.1038/s42256-021-00392-1

[153] P. Hill et al. Deep learning techniques for atmospheric turbulence removal: a review(2024).

[154] K. Wang et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2, 8(2021). https://doi.org/10.1186/s43074-021-00030-4

[155] H. Hai et al. Cryptanalysis of random-phase-encoding-based optical cryptosystem via deep learning. Opt. Express, 27, 21204-21213(2019). https://doi.org/10.1364/OE.27.021204

[156] M. Liao et al. Deep-learning-based ciphertext-only attack on optical double random phase encryption. Opto-Electron. Adv., 4, 200016(2021). https://doi.org/10.29026/oea.2021.200016

[157] H. Wu et al. Ciphertext-only attack on optical cryptosystem with spatially incoherent illumination based deep-learning correlography. Opt. Lasers Eng., 138, 106454(2021). https://doi.org/10.1016/j.optlaseng.2020.106454

[158] K. Akiyama et al. First M87 Event Horizon Telescope results. IV. Imaging the central supermassive black hole. Astrophys. J. Lett., 875, L4(2019). https://doi.org/10.3847/2041-8213/ab0e85

[159] S. Li, G. Barbastathis. Spectral pre-modulation of training examples enhances the spatial resolution of the phase extraction neural network (PhENN). Opt. Express, 26, 29340-29352(2018). https://doi.org/10.1364/OE.26.029340

[160] M. Deng et al. On the interplay between physical and content priors in deep learning for computational imaging. Opt. Express, 28, 24152-24170(2020). https://doi.org/10.1364/OE.395204

[161] K. Zhang, W. Zuo, L. Zhang. FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process., 27, 4608-4622(2018). https://doi.org/10.1109/TIP.2018.2839891

[162] M. Deng et al. Probing shallower: perceptual loss trained phase extraction neural network (PLT-Phenn) for artifact-free reconstruction at low photon budget. Opt. Express, 28, 2511-2535(2020). https://doi.org/10.1364/OE.381301

[163] Y. C. Ho, D. L. Pepyne, M. A. Simaan. Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theory Appl., 115, 549-570(2002). https://doi.org/10.1023/A:1021251113462

[164] H. Wang et al. Scientific discovery in the age of artificial intelligence. Nature, 620, 47-60(2023). https://doi.org/10.1038/s41586-023-06221-2

[165] M. I. Razzak, S. Naz, A. Zaib. Deep learning for medical image processing: overview, challenges and future(2017).

[166] C. E. Leiserson et al. There’s plenty of room at the top: what will drive computer performance after Moore’s law?. Science, 368, eaam9744(2020). https://doi.org/10.1126/science.aam9744

[167] H. N. Khan, D. A. Hounshell, E. R. H. Fuchs. Science and research policy at the end of Moore’s law. Nat. Electron., 1, 14-21(2018). https://doi.org/10.1038/s41928-017-0005-9

[168] Y. Ba, G. Zhao, A. Kadambi. Blending diverse physical priors with neural networks(2019).

[169] G. Situ. The use of deep learning for computational optical imaging: from data driven to physics driven. Proc. SPIE, 12618, 1261802(2023). https://doi.org/10.1117/12.2681500

[170] A. Kadambi et al. Incorporating physics into data-driven computer vision. Nat. Mach. Intell., 5, 572-580(2023). https://doi.org/10.1038/s42256-023-00662-0

[171] J. W. Goodman. Introduction to Fourier Optics(2005).

[172] B. I. Erkmen, J. H. Shapiro. Ghost imaging: from quantum to classical to computational. Adv. Opt. Photonics, 2, 405-450(2010). https://doi.org/10.1364/AOP.2.000405

[173] J. Mertz. Introduction to Optical Microscopy(2019).

[174] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758-2769(1982). https://doi.org/10.1364/AO.21.002758

[175] K. Yanny et al. Deep learning for fast spatially varying deconvolution. Optica, 9, 96-99(2022). https://doi.org/10.1364/OPTICA.442438

[176] Y. Xue et al. Deep-learning-augmented computational miniature mesoscope. Optica, 9, 1009-1021(2022). https://doi.org/10.1364/OPTICA.464700

[177] Q. Sun et al. End-to-end complex lens design with differentiable ray tracing. ACM Trans. Graph., 40, 71(2021). https://doi.org/10.1145/3450626.3459674

[178] V. Sitzmann et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph., 37, 114(2018). https://doi.org/10.1145/3197517.3201333

[179] Y. Peng et al. Learned large field-of-view imaging with thin-plate optics. ACM Trans. Graph., 38, 219(2019). https://doi.org/10.1145/3355089.3356526

[180] W. Huang et al. Learning-based adaptive under-sampling for Fourier single-pixel imaging. Opt. Lett., 48, 2985-2988(2023). https://doi.org/10.1364/OL.486416

[181] M. R. Kellman et al. Physics-based learned design: optimized coded-illumination for quantitative phase imaging. IEEE Trans. Comput. Imaging, 5, 344-353(2019). https://doi.org/10.1109/TCI.2019.2905434

[182] S.-H. Baek et al. Single-shot hyperspectral-depth imaging with learned diffractive optics, 2651-2660(2021). https://doi.org/10.1109/ICCV48922.2021.00265

[183] X. Yuan et al. Plug-and-play algorithms for large-scale snapshot compressive imaging, 1444-1454(2020). https://doi.org/10.1109/CVPR42600.2020.00152

[184] X. Chang, L. Bian, J. Zhang. Large-scale phase retrieval. eLight, 1, 4(2021). https://doi.org/10.1186/s43593-021-00004-w

[185] Z. Wu et al. Simba: scalable inversion in optical tomography using deep denoising priors. IEEE J. Sel. Top. Signal Process., 14, 1163-1175(2020). https://doi.org/10.1109/JSTSP.2020.2999820

[186] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018). https://doi.org/10.1126/science.aat8084

[187] M. S. Sakib Rahman, A. Ozcan. Computer-free, all-optical reconstruction of holograms using diffractive networks. ACS Photonics, 8, 3375-3384(2021). https://doi.org/10.1021/acsphotonics.1c01365

[188] E. Goi, S. Schoenhardt, M. Gu. Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks. Nat. Commun., 13, 7531(2022). https://doi.org/10.1038/s41467-022-35349-4

[189] D. Mengu, A. Ozcan. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater., 10, 2200281(2022). https://doi.org/10.1002/adom.202200281

[190] Y. Luo et al. Computational imaging without a computer: seeing through random diffusers at the speed of light. eLight, 2, 4(2022). https://doi.org/10.1186/s43593-022-00012-4

[191] Ç. Isl et al. Super-resolution image display using diffractive decoders. Sci. Adv., 8, eadd3433(2022).

[192] A. Goy et al. Low photon count phase retrieval using deep learning. Phys. Rev. Lett., 121, 243902(2018). https://doi.org/10.1103/PhysRevLett.121.243902

[193] H. Chen et al. Fourier imager network (FIN): a deep neural network for hologram reconstruction with superior external generalization. Light: Sci. Appl., 11, 254(2022). https://doi.org/10.1038/s41377-022-00949-8

[194] 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

[195] X. Zhao et al. Partially interpretable image deconvolution framework based on the Richardson-Lucy model. Opt. Lett., 48, 940-943(2023). https://doi.org/10.1364/OL.478885

[196] K. Hammernik et al. Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med., 79, 3055-3071(2018). https://doi.org/10.1002/mrm.26977

[197] J. Adler, O. Öktem. Learned primal-dual reconstruction. IEEE Trans. Med. Imaging, 37, 1322-1332(2018). https://doi.org/10.1109/TMI.2018.2799231

[198] K. C. Zhou, R. Horstmeyer. Diffraction tomography with a deep image prior. Opt. Express, 28, 12872-12896(2020). https://doi.org/10.1364/OE.379200

[199] E. Bostan et al. Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network. Optica, 7, 559-562(2020). https://doi.org/10.1364/OPTICA.389314

[200] X. Zhang, F. Wang, G. Situ. Blindnet: an untrained learning approach toward computational imaging with model uncertainty. J. Phys. D: Appl. Phys., 55, 034001(2021). https://doi.org/10.1088/1361-6463/ac2ad4

[201] R. Liu et al. Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nat. Mach. Intell., 4, 781-791(2022). https://doi.org/10.1038/s42256-022-00530-3

[202] B. Y. Feng et al. Neuws: neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media. Sci. Adv., 9, eadg4671(2023). https://doi.org/10.1126/sciadv.adg4671

[203] R. Cao et al. Neural space-time model for dynamic multi-shot imaging. Nat. Methods, 21, 2336-2341(2024). https://doi.org/10.1038/s41592-024-02417-0

[204] Z. Zhang et al. Fourier phase retrieval using physics-enhanced deep learning. Opt. Lett., 49, 6129-6132(2024). https://doi.org/10.1364/OL.537792

[205] L. Wu et al. Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks. NPJ Comput. Mater., 7, 175(2021). https://doi.org/10.1038/s41524-021-00644-z

[206] Z. Tang et al. DeepSci: scalable speckle correlation imaging using physics-enhanced deep learning. Opt. Lett., 48, 2285-2288(2023). https://doi.org/10.1364/OL.484867

[207] K. Wang et al. Deep learning spatial phase unwrapping: a comparative review. Adv. Photonics Nexus, 1, 014001(2022). https://doi.org/10.1117/1.APN.1.1.014001

[208] K. Wang, E. Y. Lam. Deep learning phase recovery: data-driven, physics-driven, or a combination of both?. Adv. Photonics Nexus, 3, 056006(2024). https://doi.org/10.1117/1.APN.3.5.056006

[209] X. Zhang et al. VgenNet: variable generative prior enhanced single pixel imaging. ACS Photonics, 10, 2363-2373(2023). https://doi.org/10.1021/acsphotonics.2c01537

[210] A. Bora et al. Compressed sensing using generative models, 537-546(2017).

[211] X. Song et al. High-resolution iterative reconstruction at extremely low sampling rate for Fourier single-pixel imaging via diffusion model. Opt. Express, 32, 3138-3156(2024). https://doi.org/10.1364/OE.510692

[212] Y. Zhang, X. Liu, E. Y. Lam. Single-shot inline holography using a physics-aware diffusion model. Opt. Express, 32, 10444-10460(2024). https://doi.org/10.1364/OE.517233

[213] F. Shamshad, F. Abbas, A. Ahmed. Deep Ptych: subsampled Fourier ptychography using generative priors, 7720-7724(2019). https://doi.org/10.1109/ICASSP.2019.8682179

[214] C. Cao et al. High-frequency space diffusion model for accelerated MRI. IEEE Trans. Med. Imaging, 43, 1853-1865(2024). https://doi.org/10.1109/TMI.2024.3351702

[215] M. Asim, F. Shamshad, A. Ahmed. Blind image deconvolution using deep generative priors. IEEE Trans. Comput. Imaging, 6, 1493-1506(2020). https://doi.org/10.1109/TCI.2020.3032671

[216] M. H. Eybposh et al. DeepCGH: 3D computer-generated holography using deep learning. Opt. Express, 28, 26636-26650(2020). https://doi.org/10.1364/OE.399624

[217] K. Liu et al. 4K-DMDNet: diffraction model-driven network for 4K computer-generated holography. Opto-Electron. Adv., 6, 220135(2023). https://doi.org/10.29026/oea.2023.220135

[218] B. Rahmani et al. Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media. Nat. Mach. Intell., 2, 403-410(2020). https://doi.org/10.1038/s42256-020-0199-9

[219] R. Li et al. Physics-enhanced neural network for phase retrieval from two diffraction patterns. Opt. Express, 30, 32680-32692(2022). https://doi.org/10.1364/OE.469080

[220] L. Huang et al. Self-supervised learning of hologram reconstruction using physics consistency. Nat. Mach. Intell., 5, 895-907(2023). https://doi.org/10.1038/s42256-023-00704-7

[221] S. Zheng et al. Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach. Opt. Express, 29, 40091-40105(2021). https://doi.org/10.1364/OE.443127

[222] Z. Liu et al. Physics-guided loss functions improve deep learning performance in inverse scattering. IEEE Trans. Comput. Imaging, 8, 236-245(2022). https://doi.org/10.1109/TCI.2022.3158865

[223] L. Shi et al. A novel loss function incorporating imaging acquisition physics for PET attenuation map generation using deep learning. Lect. Notes Comput. Sci., 11767, 723-731(2019).

[224] S. Gupta et al. Differentiable uncalibrated imaging. IEEE Trans. Comput. Imaging, 10, 1-16(2024). https://doi.org/10.1109/TCI.2023.3346294

[225] J. Liu, C. Qin, M. Yaghoobi. High-fidelity MRI reconstruction using adaptive spatial attention selection and deep data consistency prior. IEEE Trans. Comput. Imaging, 9, 298-313(2023). https://doi.org/10.1109/TCI.2023.3258839

[226] S. Wang et al. Parcel: physics-based unsupervised contrastive representation learning for multi-coil MR imaging. IEEE/ACM Trans. Comput. Biol. Bioinform., 20, 2659-2670(2023). https://doi.org/10.1109/TCBB.2022.3213669

[227] Y. Wan et al. Reconstructive spectrum analyzer with high-resolution and large-bandwidth using physical-model and data-driven model combined neural network. Laser Photonics Rev., 17, 2201018(2023). https://doi.org/10.1002/lpor.202201018

[228] Y. Jin et al. Pentagon: physics-enhanced neural network for volumetric flame chemiluminescence tomography. Opt. Express, 32, 32732-32752(2024). https://doi.org/10.1364/OE.536550

[229] H. Tu et al. Deep empirical neural network for optical phase retrieval over a scattering medium. Nat. Commun., 16, 1369(2025). https://doi.org/10.1038/s41467-025-56522-5

[230] A. Matlock, L. Tian. Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples(2021).

[231] S. Van der Jeught, J. J. Dirckx. Deep neural networks for single shot structured light profilometry. Opt. Express, 27, 17091-17101(2019). https://doi.org/10.1364/OE.27.017091

[232] D. R. Rutkowski, A. Roldán-Alzate, K. M. Johnson. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci. Rep., 11, 10240(2021). https://doi.org/10.1038/s41598-021-89636-z

[233] K. Wang et al. One-step robust deep learning phase unwrapping. Opt. Express, 27, 15100-15115(2019). https://doi.org/10.1364/OE.27.015100

[234] L. Bian et al. High-resolution single-photon imaging with physics-informed deep learning. Nat. Commun., 14, 5902(2023). https://doi.org/10.1038/s41467-023-41597-9

[235] S. Kim, S. Jung, J. Yoon. Study of a deep learning-based method for improving the spectral resolution of the spectral scanning hyperspectral imaging system via synthetic spectral image data. J. Phys. D: Appl. Phys., 56, 054005(2023). https://doi.org/10.1088/1361-6463/acae31

[236] H. Wang et al. Local conditional neural fields for versatile and generalizable large-scale reconstructions in computational imaging(2023).

[237] X. Dun et al. Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging. Optica, 7, 913-922(2020). https://doi.org/10.1364/OPTICA.394413

[238] C. A. Metzler et al. Deep optics for single-shot high-dynamic-range imaging, 1375-1385(2020). https://doi.org/10.1109/CVPR42600.2020.00145

[239] Y. Wu et al. PhaseCAM3D—learning phase masks for passive single view depth estimation, 1-12(2019). https://doi.org/10.1109/ICCPHOT.2019.8747330

[240] Y. Cheng et al. Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy. Opt. Express, 27, 644-656(2019). https://doi.org/10.1364/OE.27.000644

[241] E. Nehme et al. DeepStorm3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods, 17, 734-740(2020). https://doi.org/10.1038/s41592-020-0853-5

[242] M. Marquez et al. Deep-learning supervised snapshot compressive imaging enabled by an end-to-end adaptive neural network. IEEE J. Sel. Top. Signal Process., 16, 688-699(2022). https://doi.org/10.1109/JSTSP.2022.3172592

[243] Q. Sun et al. End-to-end learned, optically coded super-resolution spad camera. ACM Trans. Graph., 39, 9(2020). https://doi.org/10.1145/3372261

[244] R. Jacome, P. Gomez, H. Arguello. Middle output regularized end-to-end optimization for computational imaging. Optica, 10, 1421-1431(2023). https://doi.org/10.1364/OPTICA.494924

[245] B. Zhang et al. End-to-end snapshot compressed super-resolution imaging with deep optics. Optica, 9, 451-454(2022). https://doi.org/10.1364/OPTICA.450657

[246] B. Shi, Q. Lian, H. Chang. Deep prior-based sparse representation model for diffraction imaging: a plug-and-play method. Signal Process., 168, 107350(2020). https://doi.org/10.1016/j.sigpro.2019.107350

[247] Z. Lai, K. Wei, Y. Fu. Deep plug-and-play prior for hyperspectral image restoration. Neurocomputing, 481, 281-293(2022). https://doi.org/10.1016/j.neucom.2022.01.057

[248] R. Ahmad et al. Plug-and-play methods for magnetic resonance imaging: using denoisers for image recovery. IEEE Signal Process. Mag., 37, 105-116(2020). https://doi.org/10.1109/MSP.2019.2949470

[249] X. Chang et al. Plug-and-play pixel super-resolution phase retrieval for digital holography. Opt. Lett., 47, 2658-2661(2022). https://doi.org/10.1364/OL.458117

[250] M. B. Alver, A. Saleem, M. Çetin. Plug-and-play synthetic aperture radar image formation using deep priors. IEEE Trans. Comput. Imaging, 7, 43-57(2021). https://doi.org/10.1109/TCI.2020.3047473

[251] Y. Tian, Y. Fu, J. Zhang. Plug-and-play algorithms for single-pixel imaging. Opt. Lasers Eng., 154, 106970(2022). https://doi.org/10.1016/j.optlaseng.2022.106970

[252] K. Ma et al. Plug-and-play algorithm for imaging through scattering media under ambient light interference. Opt. Lett., 48, 1754-1757(2023). https://doi.org/10.1364/OL.485417

[253] D. Mengu et al. Snapshot multispectral imaging using a diffractive optical network. Light: Sci. Appl., 12, 86(2023). https://doi.org/10.1038/s41377-023-01135-0

[254] C.-Y. Shen et al. Multispectral quantitative phase imaging using a diffractive optical network. Adv. Intell. Syst., 5, 2300300(2023). https://doi.org/10.1002/aisy.202300300

[255] Ç. Işıl et al. All-optical image denoising using a diffractive visual processor. Light: Sci. Appl., 13, 43(2024). https://doi.org/10.1038/s41377-024-01385-6

[256] Y. Li et al. Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network. Light: Adv. Manuf., 4, 206(2023).

[257] I. Kang, F. Zhang, G. Barbastathis. Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts. Opt. Express, 28, 21578-21600(2020). https://doi.org/10.1364/OE.397430

[258] D. Yang et al. Fourier ptychography multi-parameter neural network with composite physical priori optimization. Biomed. Opt. Express, 13, 2739-2753(2022). https://doi.org/10.1364/BOE.456380

[259] M. Qiao et al. Deep learning for video compressive sensing. APL Photonics, 5, 030801(2020). https://doi.org/10.1063/1.5140721

[260] I. Kang et al. Attentional ptycho-tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time. Light: Sci. Appl., 12, 131(2022). https://doi.org/10.1038/s41377-023-01181-8

[261] Z. Guo et al. Physics-assisted generative adversarial network for X-ray tomography. Opt. Express, 30, 23238-23259(2022). https://doi.org/10.1364/OE.460208

[262] X. Su et al. Multi-scale iterative model-guided unfolding network for NLOS reconstruction. Comput. Graph. Forum, 42, e14958(2023). https://doi.org/10.1111/cgf.14958

[263] L. Kang et al. SAR imaging based on deep unfolded network with approximated observation. IEEE Trans. Geosci. Remote Sens., 60, 5228514(2022). https://doi.org/10.1109/TGRS.2022.3177927

[264] Z. Meng, X. Yuan, S. Jalali. Deep unfolding for snapshot compressive imaging. Int. J. Comput. Vision, 131, 2933-2958(2023). https://doi.org/10.1007/s11263-023-01844-4

[265] L. Wang et al. DNU: deep non-local unrolling for computational spectral imaging, 1658-1668(2020). https://doi.org/10.1109/CVPR42600.2020.00173

[266] Y. Li et al. End-to-end video compressive sensing using Anderson-accelerated unrolled networks, 1-12(2020). https://doi.org/10.1109/ICCP48838.2020.9105237

[267] Y. He et al. ADMMNet-based deep unrolling method for ghost imaging. IEEE Trans. Comput. Imaging, 10, 233-245(2024). https://doi.org/10.1109/TCI.2024.3361770

[268] J. Song, B. Chen, J. Zhang. Memory-augmented deep unfolding network for compressive sensing, 4249-4258(2021). https://doi.org/10.1145/3474085.3475562

[269] P. Wang, X. Yuan. SAUNet: spatial-attention unfolding network for image compressive sensing, 5099-5108(2023). https://doi.org/10.1145/3581783.3612242

[270] D. Yang et al. Dynamic coherent diffractive imaging with a physics-driven untrained learning method. Opt. Express, 29, 31426-31442(2021). https://doi.org/10.1364/OE.433507

[271] Q. Chen, D. Huang, R. Chen. Fourier ptychographic microscopy with untrained deep neural network priors. Opt. Express, 30, 39597-39612(2022). https://doi.org/10.1364/OE.472171

[272] H. Yu et al. Untrained deep learning-based fringe projection profilometry. APL Photonics, 7, 016102(2022). https://doi.org/10.1063/5.0069386

[273] Y. Wang et al. Unsupervised deep learning enables 3D imaging for single-shot incoherent holography. Laser Photonics Rev., 18, 2301091(2024). https://doi.org/10.1002/lpor.202301091

[274] 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

[275] J. Liu et al. Solving inverse problems using self-supervised deep neural nets, CTh5A.2(2021). https://doi.org/10.1364/COSI.2021.CTh5A.2

[276] Y. He et al. Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels. Adv. Photonics Nexus, 2, 046005(2023). https://doi.org/10.1117/1.APN.2.4.046005

[277] F. Han et al. Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder. Adv. Photonics Nexus, 2, 036009(2023). https://doi.org/10.1117/1.APN.2.3.036009

[278] F. Yang et al. Robust phase unwrapping via deep image prior for quantitative phase imaging. IEEE Trans. Image Process., 30, 7025-7037(2021). https://doi.org/10.1109/TIP.2021.3099956

[279] M. Qiao, X. Liu, X. Yuan. Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks. Opt. Lett., 46, 1888-1891(2021). https://doi.org/10.1364/OL.420139

[280] Y. Jin et al. Neural-field-assisted transport-of-intensity phase microscopy: partially coherent quantitative phase imaging under unknown defocus distance. Photonics Res., 12, 1494-1501(2024). https://doi.org/10.1364/PRJ.521056

[281] S. Li et al. Dynamic quantitative phase imaging using deep spatial-temporal prior. Opt. Express, 33, 7482-7491(2025). https://doi.org/10.1364/OE.545458

[282] S. Lin et al. Learning-based high-speed single-pixel imaging using a cyclic random mask. Opt. Express, 33, 25728-25742(2025). https://doi.org/10.1364/OE.562424

[283] A. Jalal, M. Ranzato et al. Robust compressed sensing MRI with deep generative priors, 14938-14954(2021).

[284] R. V. Marinescu, D. Moyer, P. Golland. Bayesian image reconstruction using deep generative models(2021).

[285] Y. Zhang et al. Physics and data-driven alternative optimization enabled ultra-low-sampling single-pixel imaging. Adv. Photonics Nexus, 4, 036005(2025). https://doi.org/10.1117/1.APN.4.3.036005

[286] Y. Peng et al. Neural holography with camera-in-the-loop training. ACM Trans. Graph., 39, 185(2020). https://doi.org/10.1145/3414685.3417802

[287] Y. Yao et al. AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging. NPJ Comput. Mater., 8, 124(2022). https://doi.org/10.1038/s41524-022-00803-w

[288] E. Cha et al. DeepPhaseCut: deep relaxation in phase for unsupervised Fourier phase retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 44, 9931-9943(2021). https://doi.org/10.1109/TPAMI.2021.3138897

[289] B. Yaman et al. Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data, 921-925(2020). https://doi.org/10.1109/ISBI45749.2020.9098514

[290] C. A. Metzler et al. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica, 7, 63-71(2020). https://doi.org/10.1364/OPTICA.374026

[291] H. Zhang et al. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network. Biomed. Opt. Express, 10, 1044-1063(2019). https://doi.org/10.1364/BOE.10.001044

[292] Q. Yang et al. Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network. Optica, 11, 860-871(2024). https://doi.org/10.1364/OPTICA.523636

[293] J. Wang et al. Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng., 35, 8312-8327(2023). https://doi.org/10.1109/TKDE.2022.3201037

[294] F. Zhuang et al. A comprehensive survey on transfer learning. Proc. IEEE, 109, 43-76(2021). https://doi.org/10.1109/JPROC.2020.3004555

[295] N. Chen et al. Differentiable imaging: a new tool for computational optical imaging. Adv. Phys. Res., 2, 2200118(2023). https://doi.org/10.1002/apxr.202200118

[296] W. Ma et al. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2021). https://doi.org/10.1038/s41566-020-0685-y

[297] Z. Shi et al. Learned multi-aperture color-coded optics for snapshot hyperspectral imaging. ACM Trans. Graph., 43, 208(2024). https://doi.org/10.1145/3687976

[298] K. Zhang et al. Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell., 44, 6360-6376(2021). https://doi.org/10.1109/TPAMI.2021.3088914

[299] U. S. Kamilov et al. Plug-and-play methods for integrating physical and learned models in computational imaging: theory, algorithms, and applications. IEEE Signal Process. Mag., 40, 85-97(2023). https://doi.org/10.1109/MSP.2022.3199595

[300] Y. Sun et al. Regularized Fourier ptychography using an online plug-and-play algorithm, 7665-7669(2019). https://doi.org/10.1109/ICASSP.2019.8683057

[301] Y. Zuo et al. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019). https://doi.org/10.1364/OPTICA.6.001132

[302] T. Wang et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photonics, 17, 408-415(2023). https://doi.org/10.1038/s41566-023-01170-8

[303] J. Liu et al. Research progress in optical neural networks: theory, applications and developments. PhotoniX, 2, 5(2021). https://doi.org/10.1186/s43074-021-00026-0

[304] B. J. Shastri et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021). https://doi.org/10.1038/s41566-020-00754-y

[305] J. Wu et al. Analog optical computing for artificial intelligence. Engineering, 10, 133-145(2022). https://doi.org/10.1016/j.eng.2021.06.021

[306] F. Tian, W. Yang. Learned lensless 3D camera. Opt. Express, 30, 34479-34496(2022). https://doi.org/10.1364/OE.465933

[307] D. Deb et al. Fouriernets enable the design of highly non-local optical encoders for computational imaging, 1-13(2024).

[308] Q. Zhang et al. Phase-shifting interferometry from single frame in-line interferogram using deep learning phase-shifting technology. Opt. Commun., 498, 127226(2021). https://doi.org/10.1016/j.optcom.2021.127226

[309] H. Luo et al. Diffraction-net: a robust single-shot holography for multi-distance lensless imaging. Opt. Express, 30, 41724-41740(2022). https://doi.org/10.1364/OE.472658

[310] D. Ulyanov, A. Vedaldi, V. Lempitsky. Deep image prior, 9446-9454(2018). https://doi.org/10.1109/CVPR.2018.00984

[311] A. Qayyum et al. Untrained neural network priors for inverse imaging problems: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 45, 6511-6536(2022). https://doi.org/10.1109/TPAMI.2022.3204527

[312] R. Heckel, P. Hand. Deep decoder: concise image representations from untrained non-convolutional networks(2018).

[313] B. Mildenhall et al. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM, 65, 99-106(2021). https://doi.org/10.1145/3503250

[314] G. E. Karniadakis et al. Physics-informed machine learning. Nat. Rev. Phys., 3, 422-440(2021). https://doi.org/10.1038/s42254-021-00314-5

[315] H. Wang et al. NeuPh: scalable and generalizable neural phase retrieval with local conditional neural fields. Adv. Photonics Nexus, 3, 056005(2024).

[316] Z. Zhao, J. C. Ye, Y. Bresler. Generative models for inverse imaging problems: from mathematical foundations to physics-driven applications. IEEE Signal Process. Mag., 40, 148-163(2023). https://doi.org/10.1109/MSP.2022.3215282

[317] Y. Liu et al. Sora: a review on background, technology, limitations, and opportunities of large vision models(2024).

[318] X. Pan et al. Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Trans. Pattern Anal. Mach. Intell., 44, 7474-7489(2021). https://doi.org/10.1109/TPAMI.2021.3115428

[319] Y. Zhang et al. Uncertainty modeling in generative compressed sensing, 26655-26668(2022).

[320] D. P. Kingma. Auto-encoding variational Bayes(2013).

[321] J. Ho, A. Jain, P. Abbeel. Denoising diffusion probabilistic models, 6840-6851(2020).

[322] J. H. R. Chang et al. One network to solve them all—solving linear inverse problems using deep projection models, 5889-5898(2017). https://doi.org/10.1109/ICCV.2017.627

[323] V. Shah, C. Hegde. Solving linear inverse problems using GAN priors: an algorithm with provable guarantees(2018).

[324] F. Shamshad, A. Ahmed. Robust compressive phase retrieval via deep generative priors(2018).

[325] P. Hand, O. Leong, V. Voroninski. Phase retrieval under a generative prior, 9154-9164(2018).

[326] X. Song et al. Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration. Photoacoustics, 33, 100558(2023). https://doi.org/10.1016/j.pacs.2023.100558

[327] R. A. Yeh et al. Semantic image inpainting with deep generative models, 6882-6890(2017). https://doi.org/10.1109/CVPR.2017.728

[328] Y. Peng et al. Neural holography, 1-2(2020). https://doi.org/10.1145/3388534.3407295

[329] X. Ma, X. Zheng, G. R. Arce. Fast inverse lithography based on dual-channel model-driven deep learning. Opt. Express, 28, 20404-20421(2020). https://doi.org/10.1364/OE.396661

[330] Q. Zhang et al. Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE). Nat. Commun., 14, 1159(2023). https://doi.org/10.1038/s41467-023-36816-2

[331] A. Saba et al. Physics-informed neural networks for diffraction tomography. Adv. Photonics, 4, 066001(2022). https://doi.org/10.1117/1.AP.4.6.066001

[332] L. V. Jospin et al. Hands-on Bayesian neural networks—a tutorial for deep learning users. IEEE Comput. Intell. Mag., 17, 29-48(2022). https://doi.org/10.1109/MCI.2022.3155327

[333] R. Shang et al. Approximating the uncertainty of deep learning reconstruction predictions in single-pixel imaging. Commun. Eng., 2, 53(2023). https://doi.org/10.1038/s44172-023-00103-1

[334] Y. Wang et al. Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv., 53, 63(2020). https://doi.org/10.1145/3386252

[335] L. Deng et al. Model compression and hardware acceleration for neural networks: a comprehensive survey. Proc. IEEE, 108, 485-532(2020). https://doi.org/10.1109/JPROC.2020.2976475

[336] D. Ghimire, D. Kil, S.-H. Kim. A survey on efficient convolutional neural networks and hardware acceleration. Electronics, 11, 945(2022). https://doi.org/10.3390/electronics11060945

[337] S. Khan et al. Transformers in vision: a survey. ACM Comput. Surv., 54, 200(2022). https://doi.org/10.1145/3505244

[338] Z. Bai et al. HoloFormer: contrastive regularization based transformer for holographic image reconstruction. IEEE Trans. Comput. Imaging, 10, 560-573(2024). https://doi.org/10.1109/TCI.2024.3384809

[339] G. Qu, P. Wang, X. Yuan. Dual-scale transformer for large-scale single-pixel imaging, 25327-25337(2024). https://doi.org/10.1109/CVPR52733.2024.02393

[340] W. Gan et al. PtychoDV: vision transformer-based deep unrolling network for ptychographic image reconstruction. IEEE Open J. Signal Process., 5, 539-547(2024). https://doi.org/10.1109/OJSP.2024.3375276

[341] X. He, K. Zhao, X. Chu. AutoML: a survey of the state-of-the-art. Knowl.-Based Syst., 212, 106622(2021). https://doi.org/10.1016/j.knosys.2020.106622

[342] R. Bommasani et al. On the opportunities and risks of foundation models(2021).

[343] W. X. Zhao et al. A survey of large language models(2023).

[344] H. Wang, L. Tian. Local conditional neural fields for versatile and generalizable large-scale reconstructions in computational imaging(2023).

[345] H. Zhou et al. Fourier ptychographic microscopy image stack reconstruction using implicit neural representations. Optica, 10, 1679-1687(2023). https://doi.org/10.1364/OPTICA.505283

[346] Y. Chen et al. All-analog photoelectronic chip for high-speed vision tasks. Nature, 623, 48-57(2023). https://doi.org/10.1038/s41586-023-06558-8

[347] B. Wen et al. Physics-driven machine learning for computational imaging from the guest editor. IEEE Signal Process. Mag., 40, 28-30(2023). https://doi.org/10.1109/MSP.2022.3222888

[348] L. Medeiros et al. The image of the M87 black hole reconstructed with PRIMO. Astrophys. J. Lett., 947, L7(2023). https://doi.org/10.3847/2041-8213/acc32d

[349] J. Jumper et al. Highly accurate protein structure prediction with alphafold. Nature, 596, 583-589(2021). https://doi.org/10.1038/s41586-021-03819-2

[350] K. He et al. Deep residual learning for image recognition, 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[351] J. Chang et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8, 12324(2018). https://doi.org/10.1038/s41598-018-30619-y

[352] G. Gallego et al. Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 44, 154-180(2022). https://doi.org/10.1109/TPAMI.2020.3008413

[353] Z. Zhang et al. Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection. Opt. Express, 28, 13269-13278(2020). https://doi.org/10.1364/OE.392370

[354] H. Fu, L. Bian, J. Zhang. Single-pixel sensing with optimal binarized modulation. Opt. Lett., 45, 3111-3114(2020). https://doi.org/10.1364/OL.395150

[355] L. Bian et al. Physical twinning for joint encoding-decoding optimization in computational optics: a review. Light: Sci. Appl., 14, 162(2025). https://doi.org/10.1038/s41377-025-01810-4

[356] H.-T. Chen, A. J. Taylor, N. Yu. A review of metasurfaces: physics and applications. Rep. Progr. Phys., 79, 076401(2016). https://doi.org/10.1088/0034-4885/79/7/076401

[357] J. Liu et al. Swept coded aperture real-time femtophotography. Nat. Commun., 15, 1589(2024). https://doi.org/10.1038/s41467-024-45820-z

[358] E. Hahamovich et al. Single pixel imaging at megahertz switching rates via cyclic Hadamard masks. Nat. Commun., 12, 4516(2021). https://doi.org/10.1038/s41467-021-24850-x

[359] P. Kilcullen, T. Ozaki, J. Liang. Compressed ultrahigh-speed single-pixel imaging by swept aggregate patterns. Nat. Commun., 13, 7879(2022). https://doi.org/10.1038/s41467-022-35585-8

[360] F. Xie et al. Subambient daytime radiative cooling of vertical surfaces. Science, 386, 788-794(2024). https://doi.org/10.1126/science.adn2524

[361] Y. Fan et al. Dispersion-assisted high-dimensional photodetector. Nature, 630, 77-83(2024). https://doi.org/10.1038/s41586-024-07398-w

[362] L. Bian et al. A broadband hyperspectral image sensor with high spatio-temporal resolution. Nature, 635, 73-81(2024). https://doi.org/10.1038/s41586-024-08109-1

[363] X. Hu et al. Metasurface-based computational imaging: a review. Adv. Photonics, 6, 014002(2024). https://doi.org/10.1117/1.AP.6.1.014002

[364] M. Pan et al. Dielectric metalens for miniaturized imaging systems: progress and challenges. Light: Sci. Appl., 11, 195(2022). https://doi.org/10.1038/s41377-022-00885-7