[1] L. Aiello et al. Green’s formulation for robust phase unwrapping in digital holography. Opt. Lasers Eng., 45, 750-755(2007).

[2] M. Jenkinson. Fast, automated, N-dimensional phase-unwrapping algorithm. Magn. Reson. Med., 49, 193-197(2003).

[3] X. Su, W. Chen. Fourier transform profilometry: a review. Opt. Lasers Eng., 35, 263-284(2001).

[4] C. Zuo et al. Deep learning in optical metrology: a review. Light Sci. Appl., 11, 39(2022).

[5] H. Yu et al. Phase unwrapping in InSAR: a review. IEEE Geosci. Remote Sens. Mag., 7, 40-58(2019).

[6] Y. Lan et al. Comparative study of DEM reconstruction accuracy between single- and multibaseline InSAR phase unwrapping. IEEE Trans. Geosci. Remote Sens., 60, 1-11(2022).

[7] K. Itoh. Analysis of the phase unwrapping algorithm. Appl. Opt., 21, 2470(1982).

[8] R. M. Goldstein, H. A. Zebker, C. L. Werner. Satellite radar interferometry: two-dimensional phase unwrapping. Radio Sci., 23, 713-720(1988).

[9] D. J. Bone. Fourier fringe analysis: the two-dimensional phase unwrapping problem. Appl. Opt., 30, 3627(1991).

[10] D. Labrousse, S. Dupont, M. Berthod. SAR interferometry: a Markovian approach to phase unwrapping with a discontinuity model. Int. Geosci. and Remote Sens. Symp., IGARSS ’95. Quant. Remote Sens. for Sci. and Appl., 556-558(1995).

[11] D. C. Ghiglia, L. A. Romero. Minimum LP-norm two-dimensional phase unwrapping. J. Opt. Soc. Am. A, 13, 1999-2013(1996).

[12] Q. Kemao. Windowed Fourier transform for fringe pattern analysis. Appl. Opt., 43, 2695(2004).

[13] Q. Kemao. Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications and implementations. Opt. Lasers Eng., 45, 304-317(2007).

[14] K. Qian. Windowed Fringe Pattern Analysis(2013).

[15] M. D. Pritt. Congruence in least-squares phase unwrapping. IGARSS’97. 1997 IEEE Int. Geosci. and Remote Sens. Symp. Proc. Remote Sens. – A Sci. Vision for Sustain. Dev., 875-877(1997).

[16] D. C. Ghiglia, M. D. Pritt. Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software(1998).

[17] X. Su, W. Chen. Reliability-guided phase unwrapping algorithm: a review. Opt. Lasers Eng., 42, 245-261(2004).

[18] E. Zappa, G. Busca. Comparison of eight unwrapping algorithms applied to Fourier-transform profilometry. Opt. Lasers Eng., 46, 106-116(2008).

[19] M. Zhao et al. Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies. Appl. Opt., 50, 6214(2011).

[20] K. H. Jin et al. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process., 26, 4509-4522(2017).

[21] W. Schwartzkopf et al. Two-dimensional phase unwrapping using neural networks. 4th IEEE Southwest Symp. Image Anal. and Interpretation, 274-277(2000).

[22] G. Dardikman, N. T. Shaked. Phase unwrapping using residual neural networks, CW3B.5(2018).

[23] G. Dardikman, N. A. Turko, N. T. Shaked. Deep learning approaches for unwrapping phase images with steep spatial gradients: a simulation, 1-4(2018).

[24] K. Wang et al. One-step robust deep learning phase unwrapping. Opt. Express, 27, 15100(2019).

[25] J. J. He et al. Deep spatiotemporal phase unwrapping of phase-contrast MRI data, 1962(2019).

[26] K. Ryu et al. Development of a deep learning method for phase unwrapping MR images, 4707(2019).

[27] G. Dardikman-Yoffe et al. PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells. Biomed. Opt. Express, 11, 1107(2020).

[28] Y. Qin et al. Direct and accurate phase unwrapping with deep neural network. Appl. Opt., 59, 7258(2020).

[29] M. V. Perera, A. De Silva. A joint convolutional and spatial quad-directional LSTM network for phase unwrapping, 4055-4059(2021).

[30] S. Park, Y. Kim, I. Moon. Automated phase unwrapping in digital holography with deep learning. Biomed. Opt. Express, 12, 7064(2021).

[31] H. Zhou et al. The PHU-NET: a robust phase unwrapping method for MRI based on deep learning. Magn. Reson. Med., 86, 3321-3333(2021).

[32] M. Xu et al. PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI. Opt. Lasers Eng., 151, 106824(2022).

[33] L. Zhou et al. PU-GAN: a one-step 2-D InSAR phase unwrapping based on conditional generative adversarial network. IEEE Trans. Geosci. Remote Sens., 60, 1-10(2022).

[34] R. Liang et al. Phase unwrapping using segmentation(2018).

[35] G. E. Spoorthi, S. Gorthi, R. K. S. S. Gorthi. PhaseNet: a deep convolutional neural network for two-dimensional phase unwrapping. IEEE Signal Process. Lett., 26, 54-58(2018).

[36] J. Zhang et al. Phase unwrapping in optical metrology via denoised and convolutional segmentation networks. Opt. Express, 27, 14903(2019).

[37] T. Zhang et al. Rapid and robust two-dimensional phase unwrapping via deep learning. Opt. Express, 27, 23173(2019).

[38] C. Wu et al. Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography. Biomed. Opt. Express, 11, 1760(2020).

[39] G. E. Spoorthi, R. K. S. S. Gorthi, S. Gorthi. PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach. IEEE Trans. Image Process., 29, 4862-4872(2020).

[40] Z. Zhao et al. Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network. Opt. Lasers Eng., 138, 106405(2020).

[41] S. Zhu et al. Phase unwrapping in ICF target interferometric measurement via deep learning. Appl. Opt., 60, 10(2021).

[42] K. S. Vengala, N. Paluru, R. K. S. S. Gorthi. 3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture. J. Opt. Soc. Am. A, 39, 167(2022).

[43] K. S. Vengala, V. Ravi, G. R. K. S. Subrahmanyam. A multi-task learning for 2D phase unwrapping in fringe projection. IEEE Signal Process. Lett., 29, 797-801(2022).

[44] J. Zhang, Q. Li. EESANet: edge-enhanced self-attention network for two-dimensional phase unwrapping. Opt. Express, 30, 10470(2022).

[45] K. Yan et al. Wrapped phase denoising using convolutional neural networks. Opt. Lasers Eng., 128, 105999(2020).

[46] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning internal representations by error propagation. California Univ. San Diego La Jolla Inst for Cognitive Science, 399-421(1988).

[47] K. He et al. Deep residual learning for image recognition, 770-778(2016).

[48] N. Navab, O. Ronneberger, P. Fischer, T. Broxet?al.. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241(2015).

[49] L.-C. Chen et al. Encoder-decoder with atrous separable convolution for semantic image segmentation, 801-818(2018).

[50] T. Pohlen et al. Full-resolution residual networks for semantic segmentation in street scenes, 3309-3318(2017).

[51] K. Wang et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. Opt. Lett., 44, 4765(2019).

[52] T. Nguyen et al. Deep learning approach for Fourier ptychography microscopy. Opt. Express, 26, 26470(2018).

[53] Y. Wu et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704(2018).

[54] N. Borhani et al. Learning to see through multimode fibers. Optica, 5, 960(2018).

[55] S. K. Devalla et al. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed. Opt. Express, 9, 3244(2018).

[56] G. Barbastathis, A. Ozcan, G. Situ. On the use of deep learning for computational imaging. Optica, 6, 921(2019).

[57] Y. Rivenson et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl., 8, 23(2019).

[58] Y. Rivenson et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng., 3, 466-477(2019).

[59] Y. Rivenson, Y. Wu, A. Ozcan. Deep learning in holography and coherent imaging. Light Sci. Appl., 8, 85(2019).

[60] S. Feng et al. Fringe pattern analysis using deep learning. Adv. Photon., 1, 025001(2019).

[61] H. Wang et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 16, 103-110(2019).

[62] J. Zhao et al. Deep-learning cell imaging through Anderson localizing optical fiber. Adv. Photon., 1, 066001(2019).

[63] W. Yin et al. Temporal phase unwrapping using deep learning. Sci. Rep., 9, 20175(2019).

[64] Z. Ren, Z. Xu, E. Y. Lam. End-to-end deep learning framework for digital holographic reconstruction. Adv. Photon., 1, 016004(2019).

[65] H. Yu et al. Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning. Opt. Express, 28, 9405-9418(2020).

[66] J. Tang et al. RestoreNet: a deep learning framework for image restoration in optical synthetic aperture imaging system. Opt. Lasers Eng., 139, 106463(2020).

[67] K. Wang et al. Transport of intensity equation from a single intensity image via deep learning. Opt. Lasers Eng., 134, 106233(2020).

[68] K. Wang et al. Y4-Net: a deep learning solution to one-shot dual-wavelength digital holographic reconstruction. Opt. Lett., 45, 4220(2020).

[69] J. Qian et al. Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt. Lett., 45, 1842-1845(2020).

[70] M. Lyu et al. Learning-based lensless imaging through optically thick scattering media. Adv. Photon., 1, 036002(2019).

[71] J. Lim, A. B. Ayoub, D. Psaltis. Three-dimensional tomography of red blood cells using deep learning. Adv. Photon., 2, 026001(2020).

[72] K. Wang et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2, 8(2021).

[73] J. Wu, L. Cao, G. Barbastathis. DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging. Opt. Lett., 46, 130(2021).

[74] J. Wu et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt. Lett., 46, 2908(2021).

[75] S. Zheng et al. Incoherent imaging through highly nonstatic and optically thick turbid media based on neural network. Photon. Res., 9, B220(2021).

[76] Y. Wu et al. Dense-U-net: dense encoder–decoder network for holographic imaging of 3D particle fields. Opt. Commun., 493, 126970(2021).

[77] M. Liao et al. Deep-learning-based ciphertext-only attack on optical double random phase encryption. Opto-Electron. Adv., 4, 200016(2021).

[78] C. Szegedy et al. Rethinking the inception architecture for computer vision. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2818-2826(2016).

[79] J. Di et al. Dual-wavelength common-path digital holographic microscopy for quantitative phase imaging based on lateral shearing interferometry. Appl. Opt., 55, 7287(2016).

[80] Y. Li et al. Quantitative phase microscopy for cellular dynamics based on transport of intensity equation. Opt. Express, 26, 586(2018).

[81] C. E. Shannon. A mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423(1948).

[82] M. Deng et al. On the interplay between physical and content priors in deep learning for computational imaging. Opt. Express, 28, 24152(2020).

[83] K. Qian. Updated WFT for fringe analysis(2017).

[84] I. Goodfellow et al. Generative adversarial nets(2014).

[85] S. Du et al. Affine iterative closest point algorithm for point set registration. Pattern Recognit. Lett., 31, 791-799(2010).

[86] M. Xin et al. A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window. Rev. Sci. Instrum., 89, 024704(2018).

[87] A. Kendall, I. Guyon, Y. Galet?al.. What uncertainties do we need in Bayesian deep learning for computer vision?. Advances in Neural Information Processing Systems(2017).

[88] Y. Chen et al. Dynamic convolution: attention over convolution kernels, 11030-11039(2020).

[89] O. Oktay et al. Attention U-net: learning where to look for the pancreas(2018).

[90] J. Yosinski, Z. Ghahramani et al. How transferable are features in deep neural networks?. Adv. in Neural Inform. Process. Syst.(2014).

[91] D. Ulyanov, A. Vedaldi, V. Lempitsky. Deep image prior, 9446-9454(2018).

[92] F. Yang et al. Robust phase unwrapping via deep image prior for quantitative phase imaging. IEEE Trans. Image Process., 30, 7025-7037(2021).

[93] F. Wang et al. Phase imaging with an untrained neural network. Light Sci. Appl., 9, 77(2020).

[94] D. Yang et al. Dynamic coherent diffractive imaging with a physics-driven untrained learning method. Opt. Express, 29, 31426(2021).

[95] F. Niknam, H. Qazvini, H. Latifi. Holographic optical field recovery using a regularized untrained deep decoder network. Sci. Rep., 11, 10903(2021).