[1] Mait J N, Euliss G W, Athale R A. Computational imaging[J]. Advances in Optics and Photonics, 10, 409-483(2018).
[2] Barbastathis G, Ozcan A, Situ G H. On the use of deep learning for computational imaging[J]. Optica, 6, 921-943(2019).
[3] Fienup J R. Reconstruction of an object from the modulus of its Fourier transform[J]. Optics Letters, 3, 27-29(1978).
[4] Fienup J R. Phase retrieval algorithms: a comparison[J]. Applied Optics, 21, 2758-2769(1982).
[5] Pittman T B, Shih Y H, Strekalov D V et al. Optical imaging by means of two-photon quantum entanglement[J]. Physical Review A, 52, R3429-R3432(1995).
[7] Natterer F. The mathematics of computerized tomography (classics in applied mathematics, vol. 32)[J]. Inverse Problems, 18, 283-284(2001).
[8] Lauterbur P C. Image formation by induced local interactions: examples employing nuclear magnetic resonance[J]. Nature, 242, 190-191(1973).
[9] Mansfield P, Grannell P K. “Diffraction” and microscopy in solids and liquids by NMR[J]. Physical Review B, 12, 3618-3634(1975).
[11] Häusler G, Willomitzer F. A stroll through 3D imaging and measurement[J]. ICO Newslett, 104(2015).
[12] Willomitzer F, Ettl S, Faber C et al. Single-shot three-dimensional sensing with improved data density[J]. Applied Optics, 54, 408-417(2015).
[13] Arimoto H, Javidi B. Integral three-dimensional imaging with digital reconstruction[J]. Optics Letters, 26, 157-159(2001).
[14] Xiao X, Javidi B, Martinez-Corral M et al. Advances in three-dimensional integral imaging: sensing, display, and applications [Invited][J]. Applied Optics, 52, 546-560(2013).
[15] Ng R. Digital light field photography[M]. USA: Stanford University(2006).
[16] Cutrona L J, Vivian W E, Leith E N et al. MIL-, 5, 127-131(1961).
[17] Betzig E, Patterson G H, Sougrat R et al. Imaging intracellular fluorescent proteins at nanometer resolution[J]. Science, 313, 1642-1645(2006).
[19] Gustafsson M G L. Nonlinear structured-illumination microscopy:wide-field fluorescence imaging with theoretically unlimited resolution[J]. Proceedings of the National Academy of Sciences of the United States of America, 102, 13081-13086(2005).
[20] Baraniuk R. Compressive sensing [lecture notes][J]. IEEE Signal Processing Magazine, 24, 118-121(2007).
[21] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[22] Goodfellow I, Bengio Y, Courville A[M]. Deep learning(2016).
[23] Sinha A, Lee J, Li S et al. Lensless computational imaging through deep learning[J]. Optica, 4, 1117-1125(2017).
[24] Xue Y, Cheng S, Li Y et al. Reliable deep-learning-based phase imaging with uncertainty quantification[J]. Optica, 6, 618-629(2019).
[25] Rivenson Y, Liu T R, Wei Z S et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning[J]. Light: Science & Applications, 8, 23(2019).
[26] Li S, Deng M, Lee J et al. Imaging through glass diffusers using densely connected convolutional networks[J]. Optica, 5, 803-813(2018).
[27] Li Y Z, Xue Y J, Tian L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media[J]. Optica, 5, 1181-1190(2018).
[29] Lyu M, Wang W, Wang H et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 7, 17865(2017).
[30] He Y C, Wang G, Dong G X et al. Ghost imaging based on deep learning[J]. Scientific Reports, 8, 6469(2018).
[31] Wang F, Wang H, Wang H C et al. Learning from simulation: an end-to-end deep-learning approach for computational ghost imaging[J]. Optics Express, 27, 25560-25572(2019).
[32] Rivenson Y, Zhang Y B, Günaydın H et al. Phase recovery and holographic image reconstruction using deep learning in neural networks[J]. Light: Science & Applications, 7, 17141(2018).
[33] Ren Z B, Xu Z M, Lam E Y. Learning-based nonparametric autofocusing for digital holography[J]. Optica, 5, 337-344(2018).
[34] Wang H, Lyu M. Situ G H. eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction[J]. Optics Express, 26, 22603-22614(2018).
[35] Nguyen T, Xue Y J, Li Y Z et al. Deep learning approach for Fourier ptychography microscopy[J]. Optics Express, 26, 26470-26484(2018).
[36] Zhang J Z, Xu T F, Shen Z Y et al. Fourier ptychographic microscopy reconstruction with multiscale deep residual network[J]. Optics Express, 27, 8612-8625(2019).
[37] Nehme E, Weiss L E, Michaeli T et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning[J]. Optica, 5, 458-464(2018).
[38] Wang H D, Rivenson Y, Jin Y Y et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy[J]. Nature Methods, 16, 103-110(2019).
[39] Liu T R, de Haan K, Rivenson Y et al. Deep learning-based super-resolution in coherent imaging systems[J]. Scientific Reports, 9, 3926(2019).
[40] Goy A, Arthur K, Li S et al. Low photon count phase retrieval using deep learning[J]. Physical Review Letters, 121, 243902(2018).
[41] Niu Z Z, Shi J H, Sun L et al. Photon-limited face image super-resolution based on deep learning[J]. Optics Express, 26, 22773-22782(2018).
[42] Wang K Q, Li Y, Qian K M et al. One-step robust deep learning phase unwrapping[J]. Optics Express, 27, 15100-15115(2019).
[43] Zhang T, Jiang S W, Zhao Z X et al. Rapid and robust two-dimensional phase unwrapping via deep learning[J]. Optics Express, 27, 23173-23185(2019).
[45] Yan K T, Yu Y J, Huang C T et al. Fringe pattern denoising based on deep learning[J]. Optics Communications, 437, 148-152(2019).
[46] Halupka K J, Antony B J, Lee M H et al. Retinal optical coherence tomography image enhancement via deep learning[J]. Biomedical Optics Express, 9, 6205-6221(2018).
[47] Choi G, Ryu D, Jo Y et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography[J]. Optics Express, 27, 4927-4943(2019).
[48] Huang Y Q, Lu Z X, Shao Z M et al. Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network[J]. Optics Express, 27, 12289-12307(2019).
[49] Borhani N, Kakkava E, Moser C et al. Learning to see through multimode fibers[J]. Optica, 5, 960-966(2018).
[50] Rahmani B, Loterie D, Konstantinou G et al. Multimode optical fiber transmission with a deep learning network[J]. Light: Science & Applications, 7, 69(2018).
[51] Bishop C M. Pattern recognition and machine learning[M]. New York: Springer-Verlag(2006).
[52] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. [C]//Advances in Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec, Canada. Canada: NIPS, 2672-2680(2014).
[53] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 2, 359-366(1989).
[54] Cybenko G. Approximation by superpositions of a sigmoidal function[J]. Mathematics of Control, Signals, and Systems, 2, 303-314(1989).
[55] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. [C]//Proceedings of the 27th international conference on machine learning (ICML-10), June 21-24, 2010, Haifa, Israel. [S.l.: s.n.], 807-814(2010).
[56] Bottou L. Large-scale machine learning with stochastic gradient descent[M]. //Lechevallier Y, Saporta G. Proceedings of COMPSTAT'2010. Heidelberg: Physica-Verlag HD, 177-186(2010).
[57] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, April 11-13, 2011, Fort Lauderdale, USA., 315-323(2011).
[58] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. [C]//2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1520-1528(2015).
[59] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).
[60] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014).
[62] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).
[64] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 323, 533-536(1986).
[65] Nielsen M A[M]. Neural networks and deep learning(2015).
[66] Wang K Q, Dou J Z, Qian K M et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction[J]. Optics Letters, 44, 4765-4768(2019).
[67] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[68] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).
[69] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. [C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, May 13-15, 2010, Chia Laguna Resort, Sardinia, Italy. [S.l.: s.n.], 249-256(2010).
[71] Wang L, Ho P P, Liu C et al. Ballistic 2-D imaging through scattering walls using an ultrafast optical Kerr gate[J]. Science, 253, 769-771(1991).
[72] Vellekoop I M, Mosk A P. Focusing coherent light through opaque strongly scattering media[J]. Optics Letters, 32, 2309-2311(2007).
[75] Gabor D. A new microscopic principle[J]. Nature, 161, 777-778(1948).
[76] Leith E N, Upatnieks J. Reconstructed wavefronts and communication theory[J]. Journal of the Optical Society of America, 52, 1123-1130(1962).
[77] Rivenson Y, Wu Y C, Ozcan A. Deep learning in holography and coherent imaging[J]. Light: Science & Applications, 8, 85(2019).
[78] Shapiro J H. Computational ghost imaging[J]. Physical Review A, 78, 061802(2008).
[79] Clemente P, Durán V, Torres-Company V et al. Optical encryption based on computational ghost imaging[J]. Optics Letters, 35, 2391-2393(2010).
[80] Cheng J. Ghost imaging through turbulent atmosphere[J]. Optics Express, 17, 7916-7921(2009).
[81] Gong W L, Zhao C Q, Yu H et al. Three-dimensional ghost imaging lidar via sparsity constraint[J]. Scientific Reports, 6, 26133(2016).
[82] Ceddia D, Paganin D M. Random-matrix bases, ghost imaging, and X-ray phase contrast computational ghost imaging[J]. Physical Review A, 97, 062119(2018).
[83] Katz O, Bromberg Y, Silberberg Y. Compressive ghost imaging[J]. Applied Physics Letters, 95, 131110(2009).