[1] Y SHECHTMAN, Y C ELDAR, O COHEN et al. Phase retrieval with application to optical imaging: A contemporary overview. IEEE Signal Processing Magazine, 32, 87-109(2015).
[2] Y PARK, C DEPEURSINGE, G POPESCU. Quantitative phase imaging in biomedicine. Nat Photonics, 12, 578-589(2018).
[3] J MIAO, P CHARALAMBOUS, J KIRZ et al. Extending the methodology of X-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens. Nature, 400, 342-344(1999).
[4] Z HUANG, L CAO. Quantitative phase imaging based on holography: Trends and new perspectives. Light Sci Appl, 13, 145(2024).
[5] M FRATZ, T BECKMANN, J ANDERS et al. Inline application of digital holography [invited]. Appl Opt, 58, G120-G126(2019).
[6] K WANG, L SONG, C WANG et al. On the use of deep learning for phase recovery. Light Sci Appl, 13, 4(2024).
[7] M V KLIBANOV, P E SACKS, A V TIKHONRAVOV. The phase retrieval problem. Inverse Problems, 11, 1(1995).
[8] D GABOR. A new microscopic principle. Nature, 161, 777-778(1948).
[9] I YAMAGUCHI, T ZHANG. Phase-shifting digital holography. Opt Lett, 22, 1268-1270(1997).
[10] R V SHACK. Production and use of a lenticular hartmann screen. Spring Meeting of Optical Society of America, 1971, 656(1971).
[11] M R TEAGUE. Deterministic phase retrieval: A green’s function solution. J Opt Soc Am, 73, 1434-1441(1983).
[12] J R FIENUP. Phase retrieval algorithms: A comparison. Appl Opt, 21, 2758-2769(1982).
[13] Y LECUN, Y BENGIO, G HINTON. Deep learning. Nature, 521, 436-444(2015).
[14] K GODA, B JALALI, C LEI et al. AI boosts photonics and vice versa. APL Photonics, 5, 070401(2020).
[15] DONG C, LOY C C, HE K, et al. Learning a deep convolutional wk f image superresolution [C]Proc of Computer Vision – ECCV 2014, 2014, 8692: 184199.
[16] LES R Q, SU H, KAICHUN M, et al. Point: Deep learning on point sets f 3D classification segmentation [C]Proc of 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR), 2017: 7785.
[17] Z WANG, J CHEN, S C H HOI. Deep learning for image super-resolution: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3365-3387(2021).
[18] G LITJENS, T KOOI, B E BEJNORDI et al. A survey on deep learning in medical image analysis. Med Image Anal, 42, 60-88(2017).
[19] T NGUYEN, V BUI, V LAM et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Opt Express, 25, 15043-15057(2017).
[20] L HUANG, T LIU, X YANG et al. Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks. ACS Photonics, 8, 1763-1774(2021).
[21] Y WU, Y RIVENSON, Y ZHANG et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704-710(2018).
[22] ZHOU WJ, LIU S, ZHANG H, et al. A deep learning approach f digital hologram speckle noise reduction [C]Proc of Imaging Applied Optics Congress, 2020.
[23] GAO Y, CAO L. Pixel superresolution quantitative phase imaging based on modulation diversity [C]Proc of SPIECOS Photonics Asia, 2022, 12318: 123180Q.
[24] K WANG, Y LI, K M QIAN et al. One-step robust deep learning phase unwrapping. Opt Express, 27, 15100-15115(2019).
[25] K LIU, J WU, Z HE et al. 4K-DMDNet: Diffraction model-driven network for 4k computer-generated holography. Opto-Electronic Advances, 6, 220135(2023).
[26] A SINHA, J LEE, S LI et al. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017).
[27] M J CHERUKARA, Y S G NASHED, R J HARDER. Real-time coherent diffraction inversion using deep generative networks. Sci Rep, 8, 16520(2018).
[28] T NGUYEN, Y XUE, Y LI et al. Deep learning approach for fourier ptychography microscopy. Opt Express, 26, 26470-26484(2018).
[29] L HU, S HU, W GONG et al. Deep learning assisted shack–hartmann wavefront sensor for direct wavefront detection. Opt Lett, 45, 3741-3744(2020).
[30] K WANG, J DI, Y LI et al. Transport of intensity equation from a single intensity image via deep learning. Optics and Lasers in Engineering, 134, 106233(2020).
[31] D PIRONE, D SIRICO, L MICCIO et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning. Lab on a Chip, 22, 793-804(2022).
[32] BA Y, ZHAO G, KADAMBI A. Blending diverse physical pris with neural wks [DBOL]. (2019101) [20241021]. https:arxiv.gabs1910.00201.
[33] F ZERNIKE. How I discovered phase contrast. Science, 121, 345-349(1955).
[34] F ZERNIKE. Phase contrast, a new method for the microscopic observation of transparent objects. Physica, 9, 686-698(1942).
[35] A NOGUCHI, H ISHIWATA, M ITOH et al. Optical sectioning in differential interference contrast microscopy. Opt Commun, 282, 3223-3230(2009).
[36] G M NOMARSKI. Differential microinterferometer with polarized waves. J Phys Radium Paris, 16, 9S(1955).
[37] X YUAN, Y XUE, J MIN et al. High-precision gaseous flame temperature field measurement based on quadriwave-lateral shearing interferometry. Optics and Lasers in Engineering, 162, 107430(2023).
[38] C ZUO, J LI, J SUN et al. Transport of intensity equation: A tutorial. Optics and Lasers in Engineering, 135, 106187(2020).
[39] J MIN, B YAO, V TRENDAFILOVA et al. Quantitative phase imaging of cells in a flow cytometry arrangement utilizing michelson interferometer-based off-axis digital holographic microscopy. J Biophotonics, 12, e201900085(2019).
[40] R W S GERCHBERG, O W. A practical algorithm for the determination of the phase from image and diffraction plane pictures. Optik, 35, 237-246(1972).
[41] T E GUREYEV, A ROBERTS, K A NUGENT. Partially coherent fields, the transport-of-intensity equation, and phase uniqueness. J Opt Soc Am A, 12, 1942-1946(1995).
[42] K Y J ZHANG, P MAIN. Histogram matching as a new density modification technique for phase refinement and extension of protein molecules. Acta Crystallographica Section A, 46, 41-46(1990).
[43] T LATYCHEVSKAIA, H-W FINK. Solution to the twin image problem in holography. Phys Rev Lett, 98, 233901(2007).
[44] ZHANG W, CAO L, BRADY D, et al. Twinimagefree holography: A compressive sensing approach [J]. Phys Rev Lett , 2018, 121(9):093902.
[45] Y RIVENSON, Y WU, H WANG et al. Sparsity-based multi-height phase recovery in holographic microscopy. Sci Rep, 6, 37862(2016).
[46] H M L FAULKNER, J M RODENBURG. Movable aperture lensless transmission microscopy: A novel phase retrieval algorithm. Phys Rev Lett, 93, 023903(2004).
[47] G ZHENG, R HORSTMEYER, C YANG. Wide-field, high-resolution fourier ptychographic microscopy. Nat Photonics, 7, 739-745(2013).
[48] Y RIVENSON, Y ZHANG, H GÜNAYDıN et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci Appl, 7, 17141(2018).
[49] 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).
[50] Z REN, Z XU, E Y LAM. End-to-end deep learning framework for digital holographic reconstruction. Advanced Photonics, 1, 016004(2019).
[51] J ZHOU, Y JIN, L LU et al. Deep learning-enabled pixel-super-resolved quantitative phase microscopy from single-shot aliased intensity measurement. Laser & Photonics Reviews, 18, 2300488(2024).
[52] D J CHANG, C M O’LEARY, C SU et al. Deep-learning electron diffractive imaging. Phys Rev Lett, 130, 016101(2023).
[53] Y WU, J WU, S JIN et al. Dense-u-net: Dense encoder–decoder network for holographic imaging of 3d particle fields. Opt Commun, 493, 126970(2021).
[54] K WANG, Q KEMAO, J DI et al. Y4-Net: A deep learning solution to one-shot dual-wavelength digital holographic reconstruction. Opt Lett, 45, 4220-4223(2020).
[55] K WANG, J DOU, K M QIAN et al. Y-Net: A one-to-two deep learning framework for digital holographic reconstruction. Opt Lett, 44, 4765-4768(2019).
[56] T ZENG, H K H SO, E Y LAM. RedCap: Residual encoder-decoder capsule network for holographic image reconstruction. Opt Express, 28, 4876-4887(2020).
[57] UELWER T, HOFFMANN T, HARMELING S. Noniterative phase retrieval with caded neural wks [C]Proc of Artificial Neural wks Machine Learning – ICANN 2021: 295306.
[58] R CASTANEDA, C TRUJILLO, A DOBLAS. Video-rate quantitative phase imaging using a digital holographic microscope and a generative adversarial network. Sensors, 21, 8021(2021).
[59] K JAFERZADEH, T FEVENS. HoloPhaseNet: Fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model. Biomed Opt Express, 13, 4032-4046(2022).
[60] LUO W, ZHANG Y, SHU X, et al. Learning endtoend phase retrieval using only one interferogram with mixedcontext wk [C]SPIE, 2022, 11970: 11970E.
[61] H DING, F LI, X CHEN et al. ContransGAN: Convolutional neural network coupling global swin-transformer network for high-resolution quantitative phase imaging with unpaired data. Cells, 11, 2394(2022).
[62] Q YE, L-W WANG, D P K LUN. SISPRNet: End-to-end learning for single-shot phase retrieval. Opt Express, 30, 31937-31958(2022).
[63] H CHEN, L HUANG, T LIU et al. Fourier imager network (FIN): A deep neural network for hologram reconstruction with superior external generalization. Light Sci Appl, 11, 254(2022).
[64] SHU X, NIU M, ZHANG Y, et al. NasPR: Neural architecture search generated phase retrieval f offaxis quantitative phase imaging [DBOL]. (20221025) [20241021]. https:arxiv.gabs2210.14231.
[65] Nakahata R, Zaman S, Zhang M, et al. Ptychofmer: A transfmerbased model f ptychographic phase retrieval [DBOL]. (20241022) [20241021]. https:arxiv.gabs2410.17377.
[66] Der Jeught S VAN, P G G MUYSHONDT, I LOBATO. Optimized loss function in deep learning profilometry for improved prediction performance. Journal of Physics: Photonics, 3, 024014(2021).
[67] X LI, H QI, S JIANG et al. Quantitative phase imaging via a cGAN network with dual intensity images captured under centrosymmetric illumination. Opt Lett, 44, 2879-2882(2019).
[68] ZHU J Y, PARK T, ISOLA P, et al. Unpaired imagetoimage translation using cycleconsistent adversarial wks [C]Proc of 2017 IEEE International Conference on Computer Vision (ICCV), 2017: 22422251.
[69] A GOY, K ARTHUR, S LI et al. Low photon count phase retrieval using deep learning. Phys Rev Lett, 121, 243902(2018).
[70] ZHANG Y, LUO W, SHU X, et al. Learning phase retrieval without using real data [C]Proc of TENCON 20222022 IEEE Region 10 Conference (TENCON), 2022: 13.
[71] WANG W, HUANG Q, YOU S, et al. Shape inpainting using 3d generative adversarial wk recurrent convolutional wks [C]Proc of 2017 IEEE International Conference on Computer Vision (ICCV), 2017: 23172325.
[72] J ZHANG, T XU, Z SHEN et al. Fourier ptychographic microscopy reconstruction with multiscale deep residual network. Opt Express, 27, 8612-8625(2019).
[73] I MOON, K JAFERZADEH, Y KIM et al. Noise-free quantitative phase imaging in gabor holography with conditional generative adversarial network. Opt Express, 28, 26284-26301(2020).
[74] H BYEON, T GO, S J LEE. Deep learning-based digital in-line holographic microscopy for high resolution with extended field of view. Optics & Laser Technology, 113, 77-86(2019).
[75] Z REN, H K H SO, E Y LAM. Fringe pattern improvement and super-resolution using deep learning in digital holography. IEEE Transactions on Industrial Informatics, 15, 6179-6186(2019).
[76] ZHOU W J, ZOU S, HE D K, et al. Speckle noise reduction in digital holograms based on spectral convolutional neural wks (scnn) [C]SPIE, 2019, 111188: 1118807.
[77] K YAN, Y YU, C HUANG et al. Fringe pattern denoising based on deep learning. Opt Commun, 437, 148-152(2019).
[78] PITKÄAHO T, MANNINEN A, NAUGHTON T J. Perfmance of autofocus capability of deep convolutional neural wks in digital holographic microscopy [C]Proc of Digital Holography ThreeDimensional Imaging, 2017.
[79] Q ZHANG, S LU, J LI et al. Phase-shifting interferometry from single frame in-line interferogram using deep learning phase-shifting technology. Opt Commun, 498, 127226(2021).
[80] Zhang Q, Lu S, Li J, et al. Deep phase shifter f quantitative phase imaging [DBOL]. (202036) [20241021]. https:arxiv.gabs2003.03027.
[81] H LUO, J XU, L ZHONG et al. Diffraction-Net: A robust single-shot holography for multi-distance lensless imaging. Opt Express, 30, 41724-41740(2022).
[82] J LI, Q ZHANG, L ZHONG et al. Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning. Opt Express, 28, 28140-28153(2020).
[83] METZLER C, SCHNITER P, VEERARAGHAVAN A, et al. Prdeep: Robust phase retrieval with a flexible deep wk [C]Proc of Proceedings of the 35th International Conference on Machine Learning, 2018, 80: 35013510.
[84] GOLDSTEIN T, STUDER C, BARANIUK R. A field guide to fwardbackward splitting with a fasta implementation [DBOL]. (20161228) [20241021]. https:arxiv.gabs1411.3406.
[85] X CHANG, L BIAN, J ZHANG. Large-scale phase retrieval. eLight, 1, 4(2021).
[86] WANG Y, SUN X, FLEISCHER J. When deep denoising meets iterative phase retrieval [C]Proc of Proceedings of the 37th International Conference on Machine Learning, 2020, 119: 1000710017.
[87] C BAI, M ZHOU, J MIN et al. Robust contrast-transfer-function phase retrieval via flexible deep learning networks. Opt Lett, 44, 5141-5144(2019).
[88] LEMPITSKY V, VEDALDI A, ULYANOV D. Deep image pri [C]Proc of 2018 IEEECVF Conference on Computer Vision Pattern Recognition, 2018: 94469454.
[89] JAGATAP G, HEGDE C. Phase retrieval using untrained neural wk pris. Wkshop on solving inverse problems with deep wks. [C]Proc of the 33rd Conference on Neural Infmation Processing Systems (Open Review, 2019), 2019.
[90] JAGATAP G, HEGDE C. Algithmic guarantees f inverse imaging with untrained wk pris [C]Proc of the 33rd International Conference on Neural Infmation Processing Systems, 2019.
[91] K C ZHOU, R HORSTMEYER. Diffraction tomography with a deep image prior. Opt Express, 28, 12872-12896(2020).
[92] L MA, H WANG, N LENG et al. ADMM based fourier phase retrieval with untrained generative prior. Journal of Computational and Applied Mathematics, 444, 115786(2024).
[93] Q CHEN, D HUANG, R CHEN. Fourier ptychographic microscopy with untrained deep neural network priors. Opt Express, 30, 39597-39612(2022).
[94] F WANG, Y BIAN, H WANG et al. Phase imaging with an untrained neural network. Light Sci Appl, 9, 77(2020).
[95] F NIKNAM, H QAZVINI, H LATIFI. Holographic optical field recovery using a regularized untrained deep decoder network. Sci Rep, 11, 10903(2021).
[96] BOOMINATHAN L, MANIPARAMBIL M, GUPTA H, et al. Phase retrieval f fourier ptychography under varying amount of measurements [C]Proc of British Machine Vision Conference, 2018.
[97] X ZHANG, F WANG, G SITU. Blindnet: An untrained learning approach toward computational imaging with model uncertainty. J Phys D: Appl Phys, 55, 034001(2022).
[98] D YANG, J ZHANG, Y TAO et al. Coherent modulation imaging using a physics-driven neural network. Opt Express, 30, 35647-35662(2022).
[99] D YANG, J ZHANG, Y TAO et al. Dynamic coherent diffractive imaging with a physics-driven untrained learning method. Opt Express, 29, 31426-31442(2021).
[100] C BAI, T PENG, J MIN et al. Dual-wavelength in-line digital holography with untrained deep neural networks. Photonics Res, 9, 2501(2021).
[101] A S GALANDE, V THAPA, H P R GURRAM et al. Untrained deep network powered with explicit denoiser for phase recovery in inline holography. Appl Phys Lett, 122, 133701(2023).
[102] X TIAN, R LI, T PENG et al. Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram. Opto-Electronic Advances, 7, 240060(2024).
[103] J ZHANG, X TAO, L YANG et al. The integration of neural network and physical reconstruction model for fourier ptychographic microscopy. Opt Commun, 504, 127470(2022).
[104] X CHEN, H WANG, A RAZI et al. DH-GAN: A physics-driven untrained generative adversarial network for holographic imaging. Opt Express, 31, 10114-10135(2023).
[105] Y JIN, L LU, S ZHOU et al. Neural-field-assisted transport-of-intensity phase microscopy: Partially coherent quantitative phase imaging under unknown defocus distance. Photonics Res, 12, 1494(2024).
[106] Y YAO, H CHAN, S SANKARANARAYANAN et al. AutoPhaseNN: unsupervised physics-aware deep learning of 3d3D nanoscale Bragg coherent diffraction imaging. npj Computational Materials Comput Mater, 8, 124(2022).
[107] R LI, G PEDRINI, Z HUANG et al. Physics-enhanced neural network for phase retrieval from two diffraction patterns. Opt Express, 30, 32680-32692(2022).
[108] C LEE, G SONG, H KIM et al. Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nature Machine Intelligence, 5, 35-45(2023).
[109] L HUANG, H CHEN, T LIU et al. Self-supervised learning of hologram reconstruction using physics consistency. Nature Machine Intelligence, 5, 895-907(2023).
[110] J WU, K LIU, X SUI et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt Lett, 46, 2908-2911(2021).
[111] G E KARNIADAKIS, I G KEVREKIDIS, L LU et al. Physics-informed machine learning. Nature Reviews Physics, 3, 422-440(2021).
[112] LIM J, PSALTIS D. Maxwell: Physicsdriven deep neural wk training based on maxwell’s equations [J]. APL Photonics , 2022, 7(1): 011301.
[113] S AMIRHOSSEIN, G CARLO, Bassam A AHMED et al. Physics-informed neural networks for diffraction tomography. Advanced Photonics, 4, 066001(2022).
[114] J ZHAO, L LIU, T WANG et al. Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning. J Biophotonics, 16, e202300090(2023).
[115] S LU, Y TIAN, Q ZHANG et al. Dynamic quantitative phase imaging based on ynet-convlstm neural network. Optics and Lasers in Engineering, 150, 106833(2022).
[116] Z GӧRӧCS, M TAMAMITSU, V BIANCO 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).
[117] V DUBEY, D POPOVA, A AHMAD et al. Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition. Sci Rep, 9, 3564(2019).
[118] S KAWATA, H-B SUN. Two-photon photopolymerization as a tool for making micro-devices. Appl Surf Sci, 208-209, 153-158(2003).
[119] M FRATZ, T SEYLER, A BERTZ et al. Digital holography in production: An overview. Light: Advanced Manufacturing, 2, 283-295(2020).
[120] L E ALTMAN, D G GRIER. Catch: Characterizing and tracking colloids holographically using deep neural networks. The Journal of Physical Chemistry B, 1602-1610(2020).
[121] Y WU, A CALIS, Y LUO et al. Label-free bioaerosol sensing using mobile microscopy and deep learning. ACS Photonics, 5, 4617-4627(2018).
[122] J ZHENG, P GAO, B YAO et al. Digital holographic microscopy with phase-shift-free structured illumination. Photonics Res, 2, 87(2014).
[123] C LIU, Z LIU, F BO et al. Super-resolution digital holographic imaging method. Appl Phys Lett, 81, 3143-3145(2002).
[124] Y GAO, L CAO. Generalized optimization framework for pixel super-resolution imaging in digital holography. Opt Express, 29, 28805-28823(2021).
[125] C DONG, C C LOY, K HE et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2014).
[126] Y RIVENSON, Z GöRöCS, H GÜNAYDIN et al. Deep learning microscopy. Optica, 4, 1437-1443(2017).
[127] H WANG, Y RIVENSON, Y JIN et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nature Methods, 16, 103-110(2019).
[128] T LIU, Haan K DE, Y RIVENSON et al. Deep learning-based super-resolution in coherent imaging systems. Sci Rep, 9, 3926(2019).
[129] JIAO Y, HE Y R, KEL M E, et al. Computational interference microscopy enabled by deep learning [J]. APL Photonics , 2021, 6(4): 046103.
[130] Z MENG, G PEDRINI, X LV et al. Dl-SI-DHM: A deep network generating the high-resolution phase and amplitude images from wide-field images. Opt Express, 29, 19247-19261(2021).
[131] A C LI, S VYAS, Y H LIN et al. Patch-based u-net model for isotropic quantitative differential phase contrast imaging. IEEE Trans Med Imaging, 40, 3229-3237(2021).
[132] Z LUO, A YURT, R STAHL et al. Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks. Opt Express, 27, 13581-13595(2019).
[133] K WANG, K QIAN, J DI et al. Deep learning spatial phase unwrapping: A comparative review. Adv Photon Nexus, 1, 014001(2022).
[134] DARDIKMAN G, SHAKED N T. Phase unwrapping using residual neural wks [C]Proc of Imaging Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), 2018.
[135] Y QIN, S WAN, Y WAN et al. Direct and accurate phase unwrapping with deep neural network. Appl Opt, 59, 7258-7267(2020).
[136] A V S VITHIN, A VISHNOI, R GANNAVARPU. Phase derivative estimation in digital holographic interferometry using a deep learning approach. Appl Opt, 61, 3061-3069(2022).
[137] S PARK, Y KIM, I MOON. Automated phase unwrapping in digital holography with deep learning. Biomed Opt Express, 12, 7064-7081(2021).
[138] H ZHOU, C CHENG, H PENG et al. The phu-net: A robust phase unwrapping method for mri based on deep learning. Magn Reson Med, 86, 3321-3333(2021).
[139] G E SPOORTHI, S GORTHI, R K S S GORTHI. Phasenet: A deep convolutional neural network for two-dimensional phase unwrapping. IEEE Signal Processing Letters, 26, 54-58(2019).
[140] S ZHU, Z ZANG, X WANG et al. Phase unwrapping in icf target interferometric measurement via deep learning. Appl Opt, 60, 10-19(2021).
[141] Z ZHAO, B LI, X KANG et al. Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network. Optics and Lasers in Engineering, 138, 106405(2021).
[142] K YAN, Y YU, T SUN et al. Wrapped phase denoising using convolutional neural networks. Optics and Lasers in Engineering, 128, 105999(2020).
[143] MICCIO L, ALFIERI D, GRILLI S, et al. Direct full compensation of the aberrations in quantitative phase microscopy of thin objects by a single digital hologram [J]. Appl Phys Lett , 2007, 90(4): 041104.
[144] C ZUO, Q CHEN, W QU et al. Phase aberration compensation in digital holographic microscopy based on principal component analysis. Opt Lett, 38, 1724-1726(2013).
[145] S MA, R FANG, Y LUO et al. Phase-aberration compensation via deep learning in digital holographic microscopy. Meas Sci Technol, 32, 105203(2021).
[146] W XIAO, L XIN, R CAO et al. Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning. Lab on a Chip, 21, 1385-1394(2021).
[147] G ZHANG, T GUAN, Z SHEN et al. Fast phase retrieval in off-axis digital holographic microscopy through deep learning. Opt Express, 26, 19388-19405(2018).
[148] J TANG, J ZHANG, S ZHANG et al. Phase aberration compensation via a self-supervised sparse constraint network in digital holographic microscopy. Optics and Lasers in Engineering, 168, 107671(2023).
[149] G WETZSTEIN, A OZCAN, S GIGAN et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020).
[150] B J SHASTRI, A N TAIT, de Lima T FERREIRA et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics, 15, 102-114(2021).
[151] D MENGU, A OZCAN. All-optical phase recovery: Diffractive computing for quantitative phase imaging. Advanced Optical Materials, 10, 2200281(2022).
[152] Rahman M S SAKIB, A OZCAN. Computer-free, all-optical reconstruction of holograms using diffractive networks. ACS Photonics, 8, 3375-3384(2021).