[1] I. Moon et al. Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy. Proc. IEEE, 97, 990-1010(2009).

[2] T.-W. Su, L. Xue, A. Ozcan. High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories. Proc. Natl. Acad. Sci. U. S. A., 109, 16018-16022(2012).

[3] F. C. Cheong et al. Flow visualization and flow cytometry with holographic video microscopy. Opt. Express, 17, 13071-13079(2009).

[4] J. Garcia-Sucerquia et al. Digital in-line holographic microscopy. Appl. Opt., 45, 836-850(2006).

[5] J. Sheng, E. Malkiel, J. Katz. Digital holographic microscope for measuring three-dimensional particle distributions and motions. Appl. Opt., 45, 3893-3901(2006).

[6] L. Tian et al. Quantitative measurement of size and three-dimensional position of fast-moving bubbles in air-water mixture flows using digital holography. Appl. Opt., 49, 1549-1554(2010).

[7] P. Picart et al. Tracking high amplitude auto-oscillations with digital Fresnel holograms. Opt. Express, 15, 8263-8274(2007).

[8] U. Schnars, W. P. Jüptner. Digital recording and numerical reconstruction of holograms. Meas. Sci. Technol., 13, R85(2002).

[9] F. Soulez et al. Inverse-problem approach for particle digital holography: accurate location based on local optimization. J. Opt. Soc. Am. A, 24, 1164-1171(2007).

[10] W. Chen et al. Empirical concentration bounds for compressive holographic bubble imaging based on a Mie scattering model. Opt. Express, 23, 4715-4725(2015).

[11] D. J. Brady et al. Compressive holography. Opt. Express, 17, 13040-13049(2009).

[12] Y. Rivenson, A. Stern, B. Javidi. Compressive Fresnel holography. J. Disp. Technol., 6, 506-509(2010).

[13] M. Born, E. Wolf. Scattering from inhomogeneous media. Principles of Optics, 695-734(2003).

[14] W. C. Chew, Y. M. Wang. Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method. IEEE Trans. Med. Imaging, 9, 218-225(1990).

[15] F.-C. Chen, W. C. Chew. Experimental verification of super resolution in nonlinear inverse scattering. Appl. Phys. Lett., 72, 3080-3082(1998).

[16] U. S. Kamilov et al. A recursive Born approach to nonlinear inverse scattering. IEEE Signal Process. Lett., 23, 1052-1056(2016).

[17] G. A. Tsihrintzis, A. J. Devaney. Higher-order (nonlinear) diffraction tomography: reconstruction algorithms and computer simulation. IEEE Trans. Image Process., 9, 1560-1572(2000).

[18] R. Pierri, G. Leone. Inverse scattering of dielectric cylinders by a second-order born approximation. IEEE Trans. Geosci. Remote Sens., 37, 374-382(1999).

[19] P. M. van den Berg, R. E. Kleinman. A contrast source inversion method. Inverse Prob., 13, 1607-1620(1997).

[20] M. T. Bevacquad et al. Non-linear inverse scattering via sparsity regularized contrast source inversion. IEEE Trans. Comput. Imaging, 3, 296-304(2017).

[21] P. M. van den Berg, A. Van Broekhoven, A. Abubakar. Extended contrast source inversion. Inverse Prob., 15, 1325-1344(1999).

[22] P. Van den Berg, A. Abubakar. Contrast source inversion method: state of art. Prog. Electromagn. Res., 34, 189-218(2001).

[23] R. E. Kleinman, P. M. van den Berg. A modified gradient method for two-dimensional problems in tomography. J. Comput. Appl. Math., 42, 17-35(1992).

[24] R. E. Kleinman, P. M. van den Berg. An extended range-modified gradient technique for profile inversion. Radio Sci., 28, 877-884(1993).

[25] H.-Y. Liu et al. SEAGLE: sparsity-driven image reconstruction under multiple scattering. IEEE Trans. Comput. Imaging, 4, 73-86(2018).

[26] Y. Ma et al. Accelerated image reconstruction for nonlinear diffractive imaging. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 6473-6477(2018).

[27] T.-A. Pham et al. Versatile reconstruction framework for diffraction tomography with intensity measurements and multiple scattering. Opt. Express, 26, 2749-2763(2018).

[28] J. Lim et al. Beyond Born–Rytov limit for super-resolution optical diffraction tomography. Opt. Express, 25, 30445-30458(2017).

[29] F. Simonetti. Multiple scattering: the key to unravel the subwavelength world from the far-field pattern of scattered wave. Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys., 73, 036619(2006).

[30] E. Mudry et al. Electromagnetic wave imaging of three-dimensional targets using a hybrid iterative inversion method. Inverse Prob., 28, 065007(2012).

[31] L. Tian, L. Waller. 3D intensity and phase imaging from light field measurements in an LED array microscope. Optica, 2, 104-111(2015).

[32] U. S. Kamilov et al. Optical tomographic image reconstruction based on beam propagation and sparse regularization. IEEE Trans. Comput. Imaging, 2, 59-70(2016).

[33] U. S. Kamilov et al. Learning approach to optical tomography. Optica, 2, 517-522(2015).

[34] M. Guizar-Sicairos, J. R. Fienup. Understanding the twin-image problem in phase retrieval. J. Opt. Soc. Am. A, 29, 2367-2375(2012).

[35] A. Beck, D. P. Palomar, Y. C. Eldar, M. Teboulle. Gradient-based algorithms with applications to signal-recovery problems. Convex Optimization in Signal Processing and Communications, 42-88(2009).

[36] W. Zhang et al. Twin-image-free holography: a compressive sensing approach. Phys. Rev. Lett., 121, 093902(2018).

[37] R. Ling et al. High-throughput intensity diffraction tomography with a computational microscope. Biomed. Opt. Express, 9, 2130-2141(2018).

[38] J. Lim et al. Learning tomography assessed using Mie theory. Phys. Rev. Appl., 9, 034027(2018).

[39] L. Rokach, O. Maimon. Clustering Methods, 321-352(2005).

[40] Gnome icon theme, ,” (accessed 3 March 2019).

[41] D. Mas et al. Fast algorithms for free-space diffraction patterns calculation. Opt. Commun., 164, 233-245(1999).

[42] A. D. Yaghjian. Electric dyadic Green’s functions in the source region. Proc. IEEE, 68, 248-263(1980).

[43] J. Van Bladel. Some remarks on Green’s dyadic for infinite space. IRE Trans. Antennas Propag., 9, 563-566(1961).

[44] A. Fannjiang. Tv-min and greedy pursuit for constrained joint sparsity and application to inverse scattering. Math. Mech. Complex Syst., 1, 81-104(2013).

[45] A. Fannjiang. Compressive sensing theory for optical systems described by a continuous model(2015).

[46] A. Beck, M. Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process., 18, 2419-2434(2009).

[47] U. S. Kamilov. A parallel proximal algorithm for anisotropic total variation minimization. IEEE Trans. Image Process., 26, 539-548(2017).

[48] N. Andrei. An acceleration of gradient descent algorithm with backtracking for unconstrained optimization. Numer. Algorithms, 42, 63-73(2006).

[49] M. Azimi, A. Kak. Distortion in diffraction tomography caused by multiple scattering. IEEE Trans. Med. Imaging, 2, 176-195(1983).

[50] S. Bolte, F. Cordelieres. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc., 224, 213-232(2006).

[51] A. Ishimaru. Wave Propagation and Scattering in Random Media, 2(1978).

[52] G. Osnabrugge, S. Leedumrongwatthanakun, I. M. Vellekoop. A convergent born series for solving the inhomogeneous Helmholtz equation in arbitrarily large media. J. Comput. Phys., 322, 113-124(2016).

[53] Y. Sun, Z. Xia, U. S. Kamilov. Efficient and accurate inversion of multiple scattering with deep learning. Opt. Express, 26, 14678-14688(2018).

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

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