[1] [1] Malamas EN, Petrakis EGM, Zervakis M, Petit L, Legat JD. A survey on industrial vision systems, applications and tools. Image Vision Comput. 2003;21(2):171–88.10.1016/S0262-8856(02)00152-X

[2] [2] Ford KR, Myer GD, Hewett TE. Reliability of landing 3D motion analysis: implications for longitudinal analyses. Med Sci Sports Exerc. 2007;39(11):2021.10.1249/mss.0b013e318149332d

[3] [3] Tiwari V, Sutton MA, McNeill SR. Assessment of High Speed Imaging Systems for 2D and 3D Deformation Measurements: Methodology Development and Validation. Exp Mech. 2007;47(4):561–79.10.1007/s11340-006-9011-y

[4] [4] Gorthi SS, Rastogi P. Fringe projection techniques: whither we are? Optics Lasers Eng. 2010;48(2):133–40.10.1016/j.optlaseng.2009.09.001

[5] [5] Li B, Wang Y, Dai J, Lohry W, Zhang S. Some recent advances on superfast 3D shape measurement with digital binary defocusing techniques. Optics Lasers Eng. 2014;54:236–46.10.1016/j.optlaseng.2013.07.010

[6] [6] Zuo C, Chen Q, Feng S, Feng F, Gu G, Sui X. Optimized pulse width modulation pattern strategy for three-dimensional profilometry with projector defocusing. Appl Opt. 2012;51(19):4477–90.10.1364/AO.51.004477

[7] [7] Heist S, Lutzke P, Schmidt I, Dietrich P, Kühmstedt P, Tünnermann A, et al. High-speed three-dimensional shape measurement using GOBO projection. Opt Lasers Eng. 2016;87:90–6.10.1016/j.optlaseng.2016.02.017

[8] [8] Heist S, Mann A, Kühmstedt P, Schreiber P, Notni G. Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement. Opt Eng. 2014;53(11):112208.10.1117/1.OE.53.11.112208

[9] [9] Caspar S, Honegger M, Rinner S, Lambelet P, Bach C, Ettemeyer A. High speed fringe projection for fast 3D inspection. In: Optical Measurement Systems for Industrial Inspection VII. vol. 8082. SPIE; 2011. p. 298–304.

[10] [10] Feng S, Zuo C, Tao T, Hu Y, Zhang M, Chen Q, et al. Robust dynamic 3-D measurements with motion-compensated phase-shifting profilometry. Optics Lasers Eng. 2018;103:127–38.10.1016/j.optlaseng.2017.12.001

[11] [11] Liu K, Wang Y, Lau DL, Hao Q, Hassebrook LG. Dual-frequency pattern scheme for high-speed 3-D shape measurement. Opt Express. 2010;18(5):5229–44.10.1364/OE.18.005229

[12] [12] Zuo C, Chen Q, Gu G, Feng S, Feng F, Li R, et al. High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection. Optics Lasers Eng. 2013;51(8):953–60.10.1016/j.optlaseng.2013.02.012

[13] [13] Tao T, Chen Q, Da J, Feng S, Hu Y, Zuo C. Real-time 3-D shape measurement with composite phase-shifting fringes and multi-view system. Opt Express. 2016;24(18):20253–69.10.1364/OE.24.020253

[14] [14] Zuo C, Tao T, Feng S, Huang L, Asundi A, Chen Q. Micro Fourier transform profilometry (μFTP): 3D shape measurement at 10,000 frames per second. Optics Lasers Eng. 2018;102:70–91.

[15] [15] Takeda M, Mutoh K. Fourier transform profilometry for the automatic measurement of 3-D object shapes. Appl Opt. 1983;22(24):3977.10.1364/AO.22.003977

[16] [16] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.10.1038/nature14539

[17] [17] Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.10.1016/j.neunet.2014.09.003

[18] [18] Zuo C, Qian J, Feng S, Yin W, Li Y, Fan P, et al. Deep learning in optical metrology: a review. Light-Sci Appl. 2022;11(1):39.10.1038/s41377-022-00714-x

[19] [19] Feng S, Chen Q, Gu G, Tao T, Zhang L, Hu Y, et al. Fringe pattern analysis using deep learning. Adv Photon. 2019;1(02):1.10.1117/1.AP.1.2.025001

[20] [20] Qian J, Feng S, Li Y, Tao T, Han J, Chen Q, et al. Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt Lett. 2020;45(7):1842–5.10.1364/OL.388994

[21] [21] Qian J, Feng S, Tao T, Hu Y, Li Y, Chen Q, et al. Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement. Apl Photon. 2020;5(4):046105.10.1063/5.0003217

[22] [22] Li Y, Qian J, Feng S, Chen Q, Zuo C. Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement. Opto-Electron Adv. 2022;5(5):210021.10.29026/oea.2022.210021

[23] [23] Li Y, Qian J, Feng S, Chen Q, Zuo C. Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement. Opt Express. 2022;30(3):3424–42.10.1364/OE.449468

[24] [24] Barbastathis G, Ozcan A, Situ G. On the use of deep learning for computational imaging. Optica. 2019;6(8):921–43.10.1364/OPTICA.6.000921

[25] [25] Shaked NT, Micó V, Trusiak M, Kuś A, Mirsky SK. Off-axis digital holographic multiplexing for rapid wavefront acquisition and processing. Adv Opt Photon. 2020;12(3):556.10.1364/AOP.384612

[26] [26] Zuo C, Feng S, Huang L, Tao T, Yin W, Chen Q. Phase shifting algorithms for fringe projection profilometry: A review. Opt Lasers Eng. 2018;109:23–59.10.1016/j.optlaseng.2018.04.019

[27] [27] Feng S, Xiao Y, Yin W, Hu Y, Li Y, Zuo C, et al. Fringe-pattern analysis with ensemble deep learning. Adv Photon Nexus. 2023;2(3):036010.10.1117/1.APN.2.3.036010

[28] [28] Gao L, Liang J, Li C, Wang LV. Single-shot compressed ultrafast photography at one hundred billion frames per second. Nature. 2014;516(7529):74–7.10.1038/nature14005

[29] [29] Yuan X, Brady DJ, Katsaggelos AK. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Proc Mag. 2021;38(2):65–88.10.1109/MSP.2020.3023869

[30] [30] He Y, Yao Y, Qi D, He Y, Huang Z, Ding P, et al. Temporal compressive super-resolution microscopy at frame rate of 1200 frames per second and spatial resolution of 100 nm. Adv Photon. 2023;5(2):026003.10.1117/1.AP.5.2.026003

[31] [31] Qiao C, Li D, Liu Y, Zhang S, Liu K, Liu C, et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat Biotechnol. 2023;41(3):367–77.10.1038/s41587-022-01471-3

[32] [32] Yin W, Che Y, Li X, Li M, Hu Y, Feng S, et al. Physics-informed deep learning for fringe pattern analysis. Opto-Electron Adv. 2024;7(1):230034–1.10.29026/oea.2024.230034

[33] [33] Weise T, Leibe B, Van Gool L. Fast 3D Scanning with Automatic Motion Compensation. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE; 2007. pp. 1–8.

[34] [34] Ibtehaz N, Rahman MS. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2020;121:74–87.10.1016/j.neunet.2019.08.025

[35] [35] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer; 2015. p. 234–241.

[36] [36] Zhang Z, Zhang B, Yuan X, Zheng S, Su X, Suo J, et al. From compressive sampling to compressive tasking: retrieving semantics in compressed domain with low bandwidth. PhotoniX. 2022;3(1):19.10.1186/s43074-022-00065-1

[37] [37] Kellman MR, Bostan E, Repina NA, Waller L. Physics-based learned design: optimized coded-illumination for quantitative phase imaging. IEEE Trans Comput Imaging. 2019;5(3):344–53.10.1109/TCI.2019.2905434

[38] [38] Wang F, Bian Y, Wang H, Lyu M, Pedrini G, Osten W, et al. Phase imaging with an untrained neural network. Light Sci Appl. 2020;9(1):77.10.1038/s41377-020-0302-3

[39] [39] Bostan E, Heckel R, Chen M, Kellman M, Waller L. Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network. Optica. 2020;7(6):559–62.10.1364/OPTICA.389314

[40] [40] Saba A, Gigli C, Ayoub AB, Psaltis D. Physics-informed neural networks for diffraction tomography. Adv Photon. 2022;4(6):066001.10.1117/1.AP.4.6.066001

[41] [41] Lin X, Rivenson Y, Yardimci NT, Veli M, Luo Y, Jarrahi M, et al. All-optical machine learning using diffractive deep neural networks. Science. 2018;361(6406):1004–8.10.1126/science.aat8084

[42] [42] Liu J, Wu Q, Sui X, Chen Q, Gu G, Wang L, et al. Research progress in optical neural networks: theory, applications and developments. PhotoniX. 2021;2:1–39.10.1186/s43074-021-00026-0

[43] [43] Luo Y, Zhao Y, Li J, Çetintaş E, Rivenson Y, Jarrahi M, et al. Computational imaging without a computer: seeing through random diffusers at the speed of light. ELight. 2022;2(1):4.10.1186/s43593-022-00012-4