S. Marchesini, Invited Article: A unified evaluation of iterative projection algorithms for phase retrieval, Review of Scientific Instruments, vol.78, issue.1, pp.11301-11302, 2007.
DOI : 10.1063/1.2403783

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao et al., Phase Retrieval with Application to Optical Imaging: A contemporary overview, IEEE Signal Processing Magazine, vol.32, issue.3, pp.87-109, 2015.
DOI : 10.1109/MSP.2014.2352673

R. W. Gerchberg and W. O. Saxton, A practical algorithm for the determination of the phase from image and diffraction plane pictures, Optik, vol.35, issue.2, pp.237-246, 1972.

J. R. Fienup, Phase retrieval algorithms: a comparison, Applied Optics, vol.21, issue.15, pp.2758-2769, 1982.
DOI : 10.1364/AO.21.002758

H. H. Bauschke, P. L. Combettes, and D. R. Luke, Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization, Journal of the Optical Society of America A, vol.19, issue.7, pp.1334-1345, 2002.
DOI : 10.1364/JOSAA.19.001334

E. J. Candés, Y. C. Eldar, T. Strohmer, and V. Voroninski, Phase Retrieval via Matrix Completion, SIAM Journal on Imaging Sciences, vol.6, issue.1, pp.199-225, 2013.
DOI : 10.1137/110848074

Y. Shechtman, Y. C. Eldar, A. Szameit, and M. Segev, Sparsity based sub-wavelength imaging with partially incoherent light via quadratic compressed sensing, Optics Express, vol.19, issue.16, pp.14807-14822, 2011.
DOI : 10.1364/OE.19.014807
URL : http://arxiv.org/abs/1104.4406

K. Jaganathan, S. Oymak, and B. Hassibi, Recovery of sparse 1-D signals from the magnitudes of their Fourier transform, 2012 IEEE International Symposium on Information Theory Proceedings, p.2012
DOI : 10.1109/ISIT.2012.6283508

H. Ohlsson, A. Y. Yang, R. Dong, and S. S. Sastry, Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming*, IFAC Proceedings Volumes, vol.45, issue.16, p.2012
DOI : 10.3182/20120711-3-BE-2027.00415
URL : http://arxiv.org/abs/1111.6323

I. Waldspurger, A. Aspremont, and S. Mallat, Phase recovery, MaxCut and complex semidefinite programming, Mathematical Programming, vol.16, issue.3, pp.47-81, 2015.
DOI : 10.1007/s10107-013-0738-9
URL : https://hal.archives-ouvertes.fr/hal-00907535

E. J. Candés, X. Li, and M. Soltanolkotabi, Phase Retrieval via Wirtinger Flow: Theory and Algorithms, IEEE Transactions on Information Theory, vol.61, issue.4, 2014.
DOI : 10.1109/TIT.2015.2399924

T. T. Cai, X. Li, and Z. Ma, Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow, The Annals of Statistics, vol.44, issue.5, p.2015
DOI : 10.1214/16-AOS1443

Y. Shechtman, A. Beck, and Y. C. Eldar, GESPAR: Efficient Phase Retrieval of Sparse Signals, IEEE Transactions on Signal Processing, vol.62, issue.4, pp.928-938, 2014.
DOI : 10.1109/TSP.2013.2297687

M. L. Moravec, J. K. Romberg, and R. G. Baraniuk, Compressive phase retrieval, Wavelets XII, pp.670120-670121, 2007.
DOI : 10.1117/12.736360

S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Applied and Numerical Harmonic Analysis, 2013.

B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, pp.607-609, 1996.
DOI : 10.1038/381607a0

M. Elad and M. Aharon, Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

J. Mairal, F. Bach, and J. Ponce, Sparse Modeling for Image and Vision Processing, Foundations and Trends?? in Computer Graphics and Vision, vol.8, issue.2-3, pp.85-283, 2014.
DOI : 10.1561/0600000058
URL : https://hal.archives-ouvertes.fr/hal-01081139

M. Aharon, M. Elad, and A. Bruckstein, <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010

A. M. Tillmann, On the Computational Intractability of Exact and Approximate Dictionary Learning, IEEE Signal Processing Letters, vol.22, issue.1, pp.45-49, 2015.
DOI : 10.1109/LSP.2014.2345761

P. Tseng and S. Yun, A coordinate gradient descent method for nonsmooth separable minimization, Mathematical Programming, vol.23, issue.1-2, pp.387-423, 2009.
DOI : 10.1007/s10107-007-0170-0

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465