[1] J. W. Goodman. Introduction to Fourier Optics(2005).

[2] V. Ntziachristos. Going deeper than microscopy: the optical imaging frontier in biology. Nat. Methods, 7, 603-614(2010).

[3] A. Ishimaru. Wave Propagation and Scattering in Random Media(1978).

[4] J. Beckers. Adaptive optics for astronomy: principles, performance, and applications. Annu. Rev. Astron. Astrophys., 31, 13-62(1993).

[5] S. Rotter, S. Gigan. Light fields in complex media: mesoscopic scattering meets wave control. Rev. Mod. Phys., 89, 015005(2017).

[6] J. W. Goodman et al. Wavefront reconstruction imaging through random media. Appl. Phys. Lett., 8, 311-313(1966).

[7] Z. Yaqoob et al. Optical phase conjugation for turbidity suppression in biological samples. Nat. Photonics, 2, 110-115(2008).

[8] Y. Zhang et al. Application of short-coherence lensless Fourier-transform digital holography in imaging through diffusive medium. Opt. Commun., 286, 56-59(2013).

[9] A. K. Singh et al. Exploiting scattering media for exploring 3D objects. Light Sci. Appl., 6, e16219(2017).

[10] I. M. Vellekoop, A. Mosk. Focusing coherent light through opaque strongly scattering media. Opt. Lett., 32, 2309-2311(2007).

[11] A. P. Mosk et al. Controlling waves in space and time for imaging and focusing in complex media. Nat. Photonics, 6, 283-292(2012).

[12] O. Katz, E. Small, Y. Silberberg. Looking around corners and through thin turbid layers in real time with scattered incoherent light. Nat. Photonics, 6, 549-553(2012).

[13] S. M. Popoff et al. Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media. Phys. Rev. Lett., 104, 100601(2010).

[14] S. Popoff et al. Image transmission through an opaque material. Nat. Commun., 1, 81(2010).

[15] M. R. I. Freund, S. Feng. Memory effects in propagation of optical waves through disordered media. Phys. Rev. Lett., 61, 2328-2331(1998).

[16] J. Bertolotti et al. Non-invasive imaging through opaque scattering layers. Nature, 491, 232-234(2012).

[17] O. Katz et al. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics, 8, 784-790(2014).

[18] G. Li et al. Cyphertext-only attack on the double random-phase encryption: experimental demonstration. Opt. Express, 25, 8690-8697(2017).

[19] W. Yang, G. Li, G. Situ. Imaging through scattering media with the auxiliary of a known reference object. Sci. Rep., 8, 9614(2018).

[20] S. Schott et al. Characterization of the angular memory effect of scattered light in biological tissues. Opt. Express, 23, 13505-13516(2015).

[21] G. Li et al. Image transmission through scattering media using ptychographic iterative engine. Appl. Sci., 9, 849(2019).

[22] I. Goodfellow, Y. Bengio, A. Courville. Deep Learning(2016).

[23] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

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

[25] M. Lyu et al. Exploit imaging through opaque wall via deep learning(2017).

[26] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl., 7, 17141(2018).

[27] A. Sinha et al. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017).

[28] K. H. Jin, M. T. McCann. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process., 26, 4509-4522(2017).

[29] P. Caramazza et al. Neural network identification of people hidden from view with a single-pixel, single-photon detector. Sci. Rep., 8, 11945(2018).

[30] S. Li et al. Imaging through glass diffusers using densely connected convolutional networks. Optica, 5, 803-813(2018).

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

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

[33] M. Lyu et al. Deep-learning-based ghost imaging. Sci. Rep., 7, 17865(2017).

[34] L. Deng. The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag., 29, 141-142(2012).

[35] I. Freund. Looking through walls and around corners. Phys. A, 168, 49-65(1990).

[36] I. Freund. Image reconstruction through multiple scattering media. Opt. Commun., 86, 216-227(1991).

[37] K. Hornik, M. Stinchcombe, H. White. Multilayer feedforward networks are universal approximators. Neural Netw., 2, 359-366(1989).

[38] G. Cybenko. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst., 2, 303-314(1989).

[39] V. Nair, G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. Proc. of the 27th Int. Conf. on Machine Learning, 807-814(2010).

[40] K. Diederik, J. Ba. Adam: a method for stochastic optimization(2014).

[41] N. Srivastava et al. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 15, 1929-1958(2014).

[42] C. M. Michail et al. Figure of image quality and information capacity in digital mammography. BioMed Res. Int., 2014, 297-308(2015).

[43] J. W. Goodman. Statistical Optics(2015).

[44] R. Michels, F. Foschum, A. Kienle. Optical properties of fat emulsions. Opt. Express, 16, 5907-5925(2008).