[1] Hamilton N. Quantification and its applications in fluorescent microscopy imaging [J]. Traffic, 2009, 10(8): 951-961. doi:  10.1111/j.1600-0854.2009.00938.x
[2] Shi R, Jin C, Xie H, et al. Multi-plane, wide-field fluorescent microscopy for biodynamic imaging in vivo [J]. Biomed Opt Express, 2019, 10(12): 6625-6635. doi:  10.1364/BOE.10.006625
[3] Goodman J W, Lawrence R. Digital image formation from electronically detected holograms [J]. Applied Physics Letters, 1967, 11(3): 77-79. doi:  10.1063/1.1755043
[4] Fan Y, Li J, Lu L, et al. Smart computational light microscopes (SCLMs) of smart computational imaging laboratory (SCILab) [J]. PhotoniX, 2021, 2(1): 1-64. doi:  10.1186/s43074-020-00023-9
[5] Gao P, Yuan C. Resolution enhancement of digital holographic microscopy via synthetic aperture: a review [J]. Light: Advanced Manufacturing, 2022, 3(1): 105-120. doi:  10.37188/lam.2022.006
[6] Gao Peng, Wen Kai, Sun Xueying, et al. Review of resolution enhancement technologies in quantitative phase microscopy [J]. Infrared and Laser Engineering, 2019, 48(6): 0603007. (in Chinese) doi:  10.3788/IRLA201948.0603007
[7] Lichtman J W, Conchello J A. Fluorescence microscopy [J]. Nature Methods, 2005, 2(12): 910-919. doi:  10.1038/nmeth817
[8] Conchello J A, Lichtman J W. Optical sectioning microscopy [J]. Nature Methods, 2005, 2(12): 920-931. doi:  10.1038/nmeth815
[9] Murfin K E, Chaston J, Goodrich-Blair H. Visualizing bacteria in nematodes using fluorescent microscopy [J]. Journal of Visualized Experiments, 2012, 68: e4298. doi:  10.3791/4298
[10] Mickoleit M, Schmid B, Weber M, et al. High-resolution reconstruction of the beating zebrafish heart [J]. Nature Methods, 2014, 11(9): 919-922. doi:  10.1038/nmeth.3037
[11] Giepmans B N, Adams S R, Ellisman M H, et al. The fluorescent toolbox for assessing protein location and function [J]. Science, 2006, 312(5771): 217-224. doi:  10.1126/science.1124618
[12] Palmer A E, Tsien R Y. Measuring calcium signaling using genetically targetable fluorescent indicators [J]. Nature Protocols, 2006, 1(3): 1057-1065. doi:  10.1038/nprot.2006.172
[13] Boulanger J, Kervrann C, Bouthemy P, et al. Patch-based nonlocal functional for denoising fluorescence microscopy image sequences [J]. IEEE Transactions on Medical Imaging, 2009, 29(2): 442-454. doi:  10.1109/TMI.2009.2033991
[14] Betzig E, Patterson G H, Sougrat R, et al. Imaging intracellular fluorescent proteins at nanometer resolution [J]. Science, 2006, 313(5793): 1642-1645. doi:  10.1126/science.1127344
[15] Hell S W, Wichmann J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluo-rescence microscopy [J]. Optics Letters, 1994, 19(11): 780-782. doi:  10.1364/OL.19.000780
[16] Gustafsson M G. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy [J]. Journal of Microscopy, 2000, 198(2): 82-87. doi:  10.1046/j.1365-2818.2000.00710.x
[17] Gao P, Prunsche B, Zhou L, et al. Background suppression in fluorescence nanoscopy with stimulated emission double depletion [J]. Nature Photonics, 2017, 11(3): 163-169. doi:  10.1038/nphoton.2016.279
[18] Klar T A, Jakobs S, Dyba M, et al. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission [J]. Proc Natl Acad Sci USA, 2000, 97(15): 8206-8210. doi:  10.1073/pnas.97.15.8206
[19] Shroff H, Galbraith C G, Galbraith J A, et al. Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics [J]. Nat Methods, 2008, 5(5): 417-423. doi:  10.1038/nmeth.1202
[20] Mertz J. Optical sectioning microscopy with planar or structured illumination [J]. Nature Methods, 2011, 8(10): 811-819. doi:  10.1038/nmeth.1709
[21] Icha J, Weber M, Waters J C, et al. Phototoxicity in live fluorescence microscopy, and how to avoid it [J]. Bioessays, 2017, 39(8): 1700003. doi:  10.1002/bies.201700003
[22] Helmerich D A, Beliu G, Matikonda S S, et al. Photoblueing of organic dyes can cause artifacts in super-resolution microscopy [J]. Nature Methods, 2021, 18(3): 253-257. doi:  10.1038/s41592-021-01061-2
[23] Wang S-C. Artificial Neural Network [M]//Interdisciplinary Computing in Java Programming. Boston, MA: Springer, 2003: 81-100.
[24] Shin H C, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning [J]. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298. doi:  10.1109/TMI.2016.2528162
[25] Wang H, Rivenson Y, Jin Y, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy [J]. Nature Methods, 2019, 16(1): 103-110. doi:  10.1038/s41592-018-0239-0
[26] Zhang H, Fang C, Xie X, et al. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network [J]. Biomed Opt Express, 2019, 10(3): 1044-1063. doi:  10.1364/BOE.10.001044
[27] Zhou H, Cai R, Quan T, et al. 3D high resolution generative deep-learning network for fluorescence microscopy imaging [J]. Optics Letters, 2020, 45(7): 1695-1698. doi:  10.1364/OL.387486
[28] Li M Z, Shan H M, Pryshchep S, et al. Deep adversarial network for super stimulated emission depletion imaging [J]. Journal of Nanophotonics, 2020, 14(1): 016009. doi:  10.1117/1.JNP.14.016009
[29] Christensen C N, Ward E N, Lio P, et al. ML-SIM: Universal reconstruction of structured illumination microscopy images using transfer learning [J]. Biomedical Optics Express, 2021, 12(5): 2720-2733. doi:  10.1364/BOE.414680
[30] Shah Z H, Müller M, Wang T C, et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images [J]. Photonics Research, 2021, 9(5): B168-B181. doi:  https://doi.org/10.1364/PRJ.416437
[31] Jin L, Liu B, Zhao F, et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed [J]. Nature Communications, 2020, 11(1): 1934. doi:  10.1038/s41467-020-15784-x
[32] Ling C, Zhang C L, Wang M Q, et al. Fast structured illumination microscopy via deep learning [J]. Photonics Research, 2020, 8(8): 1350-1359. doi:  10.1364/PRJ.396122
[33] Boyd N, Jonas E, Babcock H, et al. DeepLoco: Fast 3D localization microscopy using neural networks [Z/OL]. bioRxiv, (2018-02-26)[2022-08-01]. https://doi.org/10.1101/267096.
[34] Nehme E, Weiss L E, Michaeli T, et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning [J]. Optica, 2018, 5(4): 458-464. doi:  10.1364/OPTICA.5.000458
[35] Speiser A, Müller L R, Hoess P, et al. Deep learning enables fast and dense single-molecule localization with high accuracy [J]. Nature Methods, 2021, 18(9): 1082-1090. doi:  10.1038/s41592-021-01236-x
[36] Weigert M, Schmidt U, Boothe T, et al. Content-aware image restoration: pushing the limits of fluorescence microscopy [J]. Nature Methods, 2018, 15(12): 1090-1097. doi:  10.1038/s41592-018-0216-7
[37] Wang Z, Zhu L, Zhang H, et al. Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning [J]. Nature Methods, 2021, 18(5): 551-556. doi:  10.1038/s41592-021-01058-x
[38] Zhang X, Chen Y, Ning K, et al. Deep learning optical-sectioning method [J]. Optics Express, 2018, 26(23): 30762-30772. doi:  10.1364/OE.26.030762
[39] Bai C, Liu C, Yu X H, et al. Imaging enhancement of light-sheet fluorescence microscopy via deep learning [J]. IEEE Photonics Technology Letters, 2019, 31(22): 1803-1806. doi:  10.1109/LPT.2019.2948030
[40] Huang L, Chen H, Luo Y, et al. Recurrent neural network-based volumetric fluorescence microscopy [J]. Light Sci Appl, 2021, 10(1): 62. doi:  10.1038/s41377-021-00506-9
[41] Wu Y C, Rivenson Y, Wang H D, et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning [J]. Nature Methods, 2019, 16(12): 1323-1331. doi:  10.1038/s41592-019-0622-5
[42] Ning K, Zhang X, Gao X, et al. Deep-learning-based whole-brain imaging at single-neuron resolution [J]. Biomedical Optics Express, 2020, 11(7): 3567-3584. doi:  10.1364/BOE.393081
[43] Bai C, Yu X, Peng T, et al. 3D imaging restoration of spinning-disk confocal microscopy via deep learning [J]. IEEE Photonics Technology Letters, 2020, 32(18): 1131-1134. doi:  10.1109/LPT.2020.3014317
[44] Zhang H, Zhao Y, Fang C, et al. Exceeding the limits of 3D fluorescence microscopy using a dual-stage-processing network [J]. Optica, 2020, 7(11): 1627-1640. doi:  10.1364/OPTICA.402046
[45] Hu L, Hu S, Gong W, et al. Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration [J]. Optics Letters, 2021, 46(9): 2055-2058. doi:  10.1364/OL.418997
[46] Xiao L, Fang C, Zhu L, et al. Deep learning-enabled efficient image restoration for 3D microscopy of turbid biological specimens [J]. Optics Express, 2020, 28(20): 30234-30247. doi:  10.1364/OE.399542
[47] Lecun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444. doi:  10.1038/nature14539
[48] Mcculloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity [J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115-133. doi:  10.1007/BF02478259
[49] Lecun Y. A theoretical framework for back-propagation[C]//Proceedings of the 1988 Connectionist Models Summer School, 1988: 21-28.
[50] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507. doi:  10.1126/science.1127647
[51] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks[C]//NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, 1: 1097–1105.
[52] Ongie G, Jalal A, Metzler C A, et al. Deep learning techniques for inverse problems in imaging [J]. IEEE Journal on Selected Areas in Information Theory, 2020, 1(1): 39-56. doi:  10.1109/JSAIT.2020.2991563
[53] Ghosh N, Bhattacharya K. Cube beam-splitter interferometer for phase shifting interferometry [J]. Journal of Optics, 2009, 38(4): 191-198. doi:  10.1007/s12596-009-0017-6
[54] Mccann M T, Jin K H, Unser M. Convolutional neural networks for inverse problems in imaging: A review [J]. IEEE Signal Processing Magazine, 2017, 34(6): 85-95. doi:  10.1109/MSP.2017.2739299
[55] O'shea K, Nash R. An introduction to convolutional neural networks [EB/OL]. (2015-11-26)[2022-08-01]. https://arxiv.org/abs/1511.08458.
[56] Pang S, Du A, Orgun M A, et al. Beyond CNNs: exploiting further inherent symmetries in medical images for segmentation [EB/OL]. (2020-05-08)[2022-08-01]. https://arxiv.org/abs/2005.03924.
[57] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing And Computer-assisted Intervention, 2015: 234-241.
[58] Feizabadi M M, Shujjat A M, Shahid S, et al. Interactive latent interpolation on MNIST dataset [EB/OL]. (2020-10-15)[2022-08-01]. https://arxiv.org/abs/2010.07581.
[59] Zhu Linlin, Han Lu, Du Hong, et al. Multi-active contour cell segmentation method based on U-Net network [J]. Infrared and Laser Engineering, 2020, 49(S1): 20200121. (in Chinese) doi:  10.3788/IRLA20200121
[60] Medsker L R, Jain L. Recurrent neural networks [J]. Design and Applications, 2001, 5: 64-67.
[61] Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780. doi:  10.1162/neco.1997.9.8.1735
[62] Vinyals O, Toshev A, Bengio S, et al. Show and tell: A neural image caption generator[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2015: 3156-3164.
[63] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks [EB/OL]. (2014-09-10)[2022-08-01]. https://arxiv.org/abs/1409.3215.
[64] Graves A. Generating sequences with recurrent neural networks [EB/OL]. (2013-08-04)[2022-08-01]. https://arxiv.org/abs/1308.0850v5.
[65] Sajjad M, Kwon S. Clustering-based speech emotion recognition by incorporating learned features and deep BiLSTM [J]. IEEE Access, 2020, 8: 79861-79875. doi:  10.1109/ACCESS.2020.2990405
[66] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets [EB/OL]. (2014-06-10)[2022-08-01]. https://arxiv.org/abs/1406.2661.
[67] Isola P, Zhu J-Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 2017: 1125-1134.
[68] Brock A, Donahue J, Simonyan K. Large scale GAN training for high fidelity natural image synthesis [EB/OL].  (2018-09-28)[2022-08-01]. https://arxiv.org/abs/1809.11096v2.
[69] Cao J, Hou L, Yang M-H, et al. Remix: Towards image-to-image translation with limited data[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 15018-15027.
[70] Wang X, Yu K, Wu S, et al. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference On Computer Vision (ECCV) Workshops, 2018.
[71] Abbe E. Beiträge zur theorie des mikroskops und der mikroskopischen wahrnehmung [J]. Archiv für Mikroskopische Anatomie, 1873, 9(1): 413-418. doi:  10.1007/BF02956173
[72] Pawley J. Handbook of Biological Confocal Microscopy [M]. New York: Springer Science & Business Media, 2006.
[73] Ji Wei, Xu Tao, Liu Bei. Super-resolution fluorescent micro-scopy: A brief introduction to the Nobel Prize in Chemistry 2014 [J]. Chinese Journal of Nature, 2014, 36(6): 404-408. (in Chinese) doi:  CNKI:SUN:ZRZZ.0.2014-06-004
[74] Rust M J, Bates M, Zhuang X J N M. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) [J]. Nature Methods, 2006, 3(10): 793-796. doi:  10.1038/nmeth929
[75] Heintzmann R, Huser T. Super-resolution structured illumi-nation microscopy [J]. Chemical Reviews, 2017, 117(23): 13890-13908. doi:  10.1021/acs.chemrev.7b00218
[76] Tam J, Merino D. Stochastic optical reconstruction microscopy (STORM) in comparison with stimulated emission depletion (STED) and other imaging methods [J]. J Neurochem, 2015, 135(4): 643-658. doi:  10.1111/jnc.13257
[77] Huang B, Bates M, Zhuang X. Super-resolution fluorescence microscopy [J]. Annu Rev Biochem, 2009, 78: 993-1016. doi:  10.1146/annurev.biochem.77.061906.092014
[78] Schermelleh L, Heintzmann R, Leonhardt H. A guide to super-resolution fluorescence microscopy [J]. J Cell Biol, 2010, 190(2): 165-175. doi:  10.1083/jcb.201002018
[79] Nguyen J P, Shipley F B, Linder A N, et al. Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans [J]. Proc Natl Acad Sci USA, 2016, 113(8): E1074-1081. doi:  10.1073/pnas.1507110112
[80] Juette M F, Gould T J, Lessard M D, et al. Three-dimensional sub-100 nm resolution fluorescence microscopy of thick samples [J]. Nature Methods, 2008, 5(6): 527-529. doi:  10.1038/nmeth.1211
[81] Prabhat P, Ram S, Ward E S, et al. Simultaneous imaging of different focal planes in fluorescence microscopy for the study of cellular dynamics in three dimensions [J]. IEEE Transactions on NanoBioscience, 2004, 3(4): 237-242. doi:  10.1109/TNB.2004.837899
[82] Johnson C, Exell J, Kuo J, et al. Continuous focal translation enhances rate of point-scan volumetric microscopy [J]. Optics Express, 2019, 27(25): 36241-36258. doi:  10.1364/OE.27.036241
[83] Li H, Guo C, Kim-Holzapfel D, et al. Fast, volumetric live-cell imaging using high-resolution light-field microscopy [J]. Biomedical Optics Express, 2019, 10(1): 29-49. doi:  10.1364/BOE.10.000029
[84] Pascucci M, Ganesan S, Tripathi A, et al. Compressive three-dimensional super-resolution microscopy with speckle-saturated fluorescence excitation [J]. Nature Communications, 2019, 10(1): 1327. doi:  10.1038/s41467-019-09297-5
[85] Gong H, Xu D, Yuan J, et al. High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level [J]. Nature Communications, 2016, 7(1): 1-12. doi:  10.1038/ncomms12142
[86] Carlton P M, Boulanger J, Kervrann C, et al. Fast live simultaneous multiwavelength four-dimensional optical microscopy [J]. Proceedings of the National Academy of Sciences, 2010, 107(37): 16016-16022. doi:  10.1073/pnas.1004037107
[87] Luisier F, Blu T, Unser M. Image denoising in mixed Poisson-Gaussian noise [J]. IEEE Trans Image Process, 2011, 20(3): 696-708. doi:  10.1109/TIP.2010.2073477
[88] Soubies E, Soulez F, Mccann M T, et al. Pocket guide to solve inverse problems with GlobalBioIm [J]. Inverse Problems, 2019, 35(10): 104006. doi:  10.1088/1361-6420/ab2ae9
[89] Arigovindan M, Fung J C, Elnatan D, et al. High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio [J]. Proceedings of the National Academy of Sciences, 2013, 110(43): 17344-17349. doi:  10.1073/pnas.1315675110
[90] Setzer S, Steidl G, Teuber T. Deblurring Poissonian images by split Bregman techniques [J]. Journal of Visual Commu-nication and Image Representation, 2010, 21(3): 193-199. doi:  10.1016/j.jvcir.2009.10.006
[91] Zhang Y, Zhu Y, Nichols E, et al. A poisson-gaussian denoising dataset with real fluorescence microscopy images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 11710-11718.
[92] Hagen G M, Bendesky J, Machado R, et al. Fluorescence microscopy datasets for training deep neural networks [J]. GigaScience, 2021, 10(5): giab032. doi:  10.1093/gigascience/giab032
[93] Belthangady C, Royer L A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction [J]. Nat Methods, 2019, 16(12): 1215-1225. doi:  10.1038/s41592-019-0458-z
[94] Christiansen E M, Yang S J, Ando D M, et al. In silico labeling: predicting fluorescent labels in unlabeled images [J]. Cell, 2018, 173(3): 792-803e719. doi:  10.1016/j.cell.2018.03.040
[95] Ounkomol C, Seshamani S, Maleckar M M, et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy [J]. Nature Methods, 2018, 15(11): 917-920. doi:  10.1038/s41592-018-0111-2
[96] Khater I M, Aroca-Ouellette S T, Meng F, et al. Caveolae and scaffold detection from single molecule localization microscopy data using deep learning [J]. PLoS One, 2019, 14(8): e0211659. doi:  10.1371/journal.pone.0211659
[97] Li X, Zhang G, Qiao H, et al. Unsupervised content-preserving transformation for optical microscopy [J]. Light Sci Appl, 2021, 10(1): 44. doi:  10.1038/s41377-021-00484-y
[98] Chen X, Kandel M E, He S, et al. Artificial confocal microscopy for deep label-free imaging [EB/OL]. (2021-10-28)[2022-08-01]. https://arxiv.org/abs/2110.14823.
[99] Robitaille L É, Durand A, Gardner M-A, et al. Learning to become an expert: Deep networks applied to super-resolution microscopy[C]//Thirty-Second AAAI Conference on Artificial Intelligence, 2018.