[1] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018).

[2] J. Wang, J. Liang, J. Cheng, Y. Guo, L. Zeng. Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. PLOS ONE, 15, e0226963(2020).

[3] Y. Xue, S. Cheng, Y. Li, L. Tian. Reliable deep-learning-based phase imaging with uncertainty quantification. Optica, 6, 618-629(2019).

[4] R. P. Millane. Phase retrieval in crystallography and optics. J. Opt. Soc. Am. A, 7, 394-411(1990).

[5] R. W. Gerchberg. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35, 237-246(1972).

[6] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758-2769(1982).

[7] Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, M. Segev. Phase retrieval with application to optical imaging: a contemporary overview. IEEE Signal Process. Mag., 32, 87-109(2015).

[8] M. S. Asif, A. Ayremlou, A. Sankaranarayanan, A. Veeraraghavan, R. G. Baraniuk. Flatcam: thin, lensless cameras using coded aperture and computation. IEEE Trans. Comput. Imaging, 3, 384-397(2016).

[9] N. Antipa, G. Kuo, R. Heckel, B. Mildenhall, E. Bostan, R. Ng, L. Waller. DiffuserCam: lensless single-exposure 3D imaging. Optica, 5, 1-9(2017).

[10] K. Wakonig, A. Diaz, A. Bonnin, M. Stampanoni, A. Bergamaschi, J. Ihli, M. Guizar-Sicairos, A. Menzel. X-ray Fourier ptychography. Sci. Adv., 5, eaav0282(2019).

[11] G. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 7, 739-745(2013).

[12] P. C. Konda, L. Loetgering, K. C. Zhou, S. Xu, A. R. Harvey, R. Horstmeyer. Fourier ptychography: current applications and future promises. Opt. Express, 28, 9603-9630(2020).

[13] Y. Xiao, L. Zhou, W. Chen. Fourier spectrum retrieval in single-pixel imaging. IEEE Photonics J., 11, 7800411(2019).

[14] M.-J. Sun, J.-M. Zhang. Single-pixel imaging and its application in three-dimensional reconstruction: a brief review. Sensors, 19, 732(2019).

[15] M. P. Edgar, G. M. Gibson, M. J. Padgett. Principles and prospects for single-pixel imaging. Nat. Photonics, 13, 13-20(2018).

[16] M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, R. G. Baraniuk. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag., 25, 83-91(2008).

[17] X. Hu, H. Zhang, Q. Zhao, P. Yu, Y. Li, L. Gong. Single-pixel phase imaging by Fourier spectrum sampling. Appl. Phys. Lett., 114, 051102(2019).

[18] H. Deng, X. Gao, M. Ma, P. Yao, Q. Guan, X. Zhong, J. Zhang. Fourier single-pixel imaging using fewer illumination patterns. Appl. Phys. Lett., 114, 221906(2019).

[19] Z. Liang, D. Yu, Z. Cheng, X. Zhai. Adaptive Fourier single-pixel imaging sampling based on frequency coefficients prediction. Opt. Eng., 59, 073105(2020).

[20] Z. Zhang, X. Ma, J. Zhong. Single-pixel imaging by means of Fourier spectrum acquisition. Nat. Commun., 6, 6225(2015).

[21] M. H. Seaberg, A. d’Aspremont, J. J. Turner. Coherent diffractive imaging using randomly coded masks. Appl. Phys. Lett., 107, 231103(2015).

[22] A. Sinha, J. Lee, S. Li, G. Barbastathis. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017).

[23] Y. Li, Y. Xue, L. Tian. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181-1190(2018).

[24] C. A. Metzler, P. Schniter, A. Veeraraghavan, R. G. Baraniuk. prDeep: robust phase retrieval with a flexible deep network. Proceedings of the 35th International Conference on Machine Learning, 3501-3510(2018).

[25] E. J. Candès, X. Li, M. Soltanolkotabi. Phase retrieval from coded diffraction patterns. Appl. Comput. Harmon. Anal., 39, 277-299(2015).

[26] M. R. Kellman, E. Bostan, N. A. Repina, L. Waller. Physics-based learned design: optimized coded-illumination for quantitative phase imaging. IEEE Trans. Comput. Imaging, 5, 344-353(2019).

[27] C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals. Understanding deep learning requires rethinking generalization. Proceedings of 5th International Conference on Learning Representations, 1-15(2016).

[28] V. Antun, F. Renna, C. Poon, B. Adcock, A. C. Hansen. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc. Natl. Acad. Sci. USA, 117, 30088-30095(2020).

[29] B. Muminov, L. T. Vuong. Fourier optical preprocessing in lieu of deep learning. Optica, 7, 1079-1088(2020).

[30] R. Horisaki, R. Takagi, J. Tanida. Learning-based imaging through scattering media. Opt. Express, 24, 13738-13743(2016).

[31] S. Malik, A. Sardana, . A keyless approach to image encryption. International Conference on Communication Systems and Network Technologies, 879-883(2012).

[32] C. A. Metzler, F. Heide, P. Rangarajan, M. M. Balaji, A. Viswanath, A. Veeraraghavan, R. G. Baraniuk. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica, 7, 63-71(2020).

[33] W. Luo, W. Alghamdi, Y. M. Lu. Optimal spectral initialization for signal recovery with applications to phase retrieval. IEEE Trans. Signal Process., 67, 2347-2356(2019).

[34] P. Netrapalli, P. Jain, S. Sanghavi. Phase retrieval using alternating minimization. IEEE Trans. Signal Process., 63, 4814-4826(2015).

[35] Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, A. Ozcan. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019).

[36] E. Khoram, A. Chen, D. Liu, L. Ying, Q. Wang, M. Yuan, Z. Yu. Nanophotonic media for artificial neural inference. Photon. Res., 7, 823-827(2019).

[37] Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, M. Soljačić. Deep learning with coherent nanophotonic circuits. Nat. Photonics, 11, 441-446(2017).

[38] G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund, M. Soljačić, C. Denz, D. A. B. Miller, D. Psaltis. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020).

[39] D. Psaltis, N. Farhat. Optical information processing based on an associative-memory model of neural nets with thresholding and feedback. Opt. Lett., 10, 98-100(1985).

[40] S. Jutamulia, F. Yu. Overview of hybrid optical neural networks. Opt. Laser Technol., 28, 59-72(1996).

[41] J. Chang, V. Sitzmann, X. Dun, W. Heidrich, G. Wetzstein. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8, 12324(2018).

[42] J. Wu, H. Zhang, W. Zhang, G. Jin, L. Cao, G. Barbastathis. Single-shot lensless imaging with Fresnel zone aperture and incoherent illumination. Light Sci. Appl., 9, 53(2020).

[43] S. Zheng, X. Zeng, L. Zha, H. Shangguan, S. Xu, D. Fan. Orthogonality of diffractive deep neural networks(2018).

[44] Q. R. Razlighi, N. Kehtarnavaz. A comparison study of image spatial entropy. Proc. SPIE, 7257, 72571X(2009).

[45] D. Terzopoulos. Regularization of inverse visual problems involving discontinuities. IEEE Trans. Pattern Anal. Mach. Intell., PAMI-8, 413-424(1986).

[46] M. Deng, S. Li, Z. Zhang, I. Kang, N. X. Fang, G. Barbastathis. On the interplay between physical and content priors in deep learning for computational imaging. Opt. Express, 28, 24152-24170(2020).

[47] V. Katkovnik, I. Shevkunov, N. V. Petrov, K. Egiazarian. Computational super-resolution phase retrieval from multiple phase-coded diffraction patterns: simulation study and experiments. Optica, 4, 786-794(2017).

[48] Y. Hua, S. Nakamura, M. S. Asif, A. C. Sankaranarayanan. SweepCam—depth-aware lensless imaging using programmable masks. IEEE Trans. Pattern Anal. Mach. Intell., 42, 1606-1617(2020).