[1] Millane R P. Phase retrieval in crystallography and optics[J]. Journal of the Optical Society of America A, 7, 394-411(1990). http://www.tandfonline.com/servlet/linkout?suffix=CIT0001&dbid=16&doi=10.1080%2F17415977.2017.1336551&key=10.1364%2FJOSAA.7.000394
[2] Akcakaya M, Tarokh V. Sparse signal recovery from a mixture of linear and magnitude-only measurements[J]. IEEE Signal Processing Letters, 22, 1220-1223(2015). http://europepmc.org/abstract/MED/29187781
[3] Jaganathan K, Oymak S, Hassibi B. Sparse phase retrieval: Uniqueness guarantees and recovery algorithms[J]. IEEE Transactions on Signal Processing, 65, 2402-2410(2017). http://ieeexplore.ieee.org/document/7829400/
[4] Maji S K, Yahia H M, Fusco T. A multifractal-based wavefront phase estimation technique for ground-based astronomical observations[J]. IEEE Transactions on Geoscience & Remote Sensing, 54, 1705-1715(2016). http://ieeexplore.ieee.org/document/7328262/
[5] Leshem B, Xu R, Dallal Y et al. Direct single-shot phase retrieval from the diffraction pattern of separated objects[J]. Nature Communications, 7, 10820(2016). http://europepmc.org/articles/PMC4764927/
[6] Gerchberg R, Saxon W. A practical algorithm for the determination of phase from image and diffraction plane pictures[J]. International Journal for Light and Electron Optics, 35, 237-250(1972). http://ci.nii.ac.jp/naid/10010556614
[7] Fienup J R. Phase retrieval algorithms: A comparison[J]. Applied Optics, 21, 2758-2769(1982). http://www.ncbi.nlm.nih.gov/pubmed/20396114
[8] Roddier F, Roddier C. Wavefront reconstruction using iterative Fourier transforms[J]. Applied Optics, 30, 1325-1327(1991). http://www.opticsinfobase.org/ao/abstract.cfm?uri=ao-30-11-1325
[9] Guo Y M, Zhang F, Song Q et al. Application of hybrid iterative algorithom in TIE phase retrieval with large defocusing distance[J]. Acta Optica Sinica, 36, 0912001(2016).
[10] Cheng H, Lü Q Q, Zhang W J et al. Phase retrieval method based on liquid crystal on silicon tunable-lens[J]. Chinese Journal of Lasers, 44, 0304001(2017).
[11] Ohlsson H, Yang A, Dong R et al. Compressive phase retrieval from squared output measurements via semidefinite programming. [C]//International Federation of Automatic Control Symposium on System Identification. Brussels, Belgium, 89-94(2012).
[12] Shechtman Y, Beck A, Eldar Y C. GESPAR: Efficient phase retrieval of sparse signals[J]. IEEE Transactions on Signal Processing, 62, 928-938(2014). http://dl.acm.org/citation.cfm?id=2711223
[13] Pedarsani R, Yin D, Lee K et al. Phasecode: Fast and efficient compressive phase retrieval based on sparse-graph codes[J]. IEEE Transactions on Information Theory, 63, 3663-3691(2017). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7028542
[14] Cheng H, Zhang Q B, Wei S. Phase retrieval based on total variation[J]. Journal of Image and Graphics, 15, 1425-1429(2010).
[15] Lian Q S, Zhao X R, Shi B S et al. Phase retrieval algorithm based on cartoon-texture model[J]. Jouenal of Electronics & Information Technology, 38, 1991-1998(2016).
[16] Candes E J, Li X, Soltanolkotabi M. Phase retrieval from coded diffraction patterns[J]. Applied and Computational Harmonic Analysis, 39, 277-299(2015). http://www.sciencedirect.com/science/article/pii/S1063520314001201
[17] Candes E J, Li X, Soltanolkotabi M. Phase retrieval via wirtinger flow: Theory and algorithms[J]. IEEE Transactions on Information Theory, 61, 1985-2007(2015). http://ieeexplore.ieee.org/document/7029630/
[18] Chen Y, Candes E J. Solving random quadratic systems of equations is nearly as easy as solving linear systems[C]. Advances in Neural Information Processing Systems, 739-747(2015).
[19] Tillmann A M, Eldar Y C, Mairal J[J]. DOLPHIn: Dictionary learning for phase retrieval IEEE Transactions on Signal Processing, 64, 6485-6500(2016).
[20] Metzler C A, Maleki A, Baraniuk R G. BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising[C]. IEEE International Conference on Image Processing, 2504-2508(2016).
[22] Yuan M, Lin Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68, 49-67(2006). http://www.jstor.org/stable/3647556
[23] Yu H J, Jiang M F, Chen H R et al. Super-pixel algorithm and group sparsity regularization method for compressed sensing MR image reconstruction[J]. Optik, 140, 392-404(2017). http://www.sciencedirect.com/science/article/pii/S0030402617304734
[24] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 60, 259-268(1992). http://dl.acm.org/citation.cfm?id=142312
[25] Huang J Z, Zhang S T, Metaxas D. Efficient MR image reconstruction for compressed MR imaging[J]. Medical Image Analysis, 15, 670-679(2011). http://www.springerlink.com/content/11377uqm5v015161
[26] Beck A, Teboulle M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems[J]. IEEE Transactions on Image Processing, 18, 2419-2434(2009). http://www.ncbi.nlm.nih.gov/pubmed/19635705
[27] Chambolle A. An algorithm for total variation minimization and applications[J]. Journal of Mathematical Imaging and Vision, 20, 89-97(2004). http://dl.acm.org/citation.cfm?id=964985
[28] Kowalski M, Siedenburg K, Dorfler M. Social sparsity! Neighborhood systems enrich structured shrinkage operators[J]. IEEE Transactions on Signal Processing, 61, 2498-2511(2013). http://ieeexplore.ieee.org/document/6473914/