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This paper develops a novel framework for phase retrieval, a problem which arises in X-ray crystallography, diffraction imaging, astronomical imaging and many other applications.
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The phase retrieval problem
M.V. Klibanov, P.E. Sacks, and A.V. Tikhonravov · 1995
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Phase reconstruction from reflectivity in fiber Bragg gratings
A. Carballar and M.A. Muriel · 1997
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Diffraction Gratings and Applications
E.G. Loewen and E. Popov · 1997
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On the rank minimization problem over a positive semidefinite linear matrix inequality
M. Mesbahi and G. P. Papavassilopoulos · 1997
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Computational study and comparisons of LFT reducibility methods
C. Beck and R. D’Andrea · 1998
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Phase retrieval from the magnitude of the Fourier transforms of nonperiodic objects
J. Miao, D. Sayre, and H.N. Chapman · 1998
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Application of the optimization technique to noncrystalline X-Ray diffraction microscopy: guided hybrid input-output method (GHIO)
C.C. Chen, J. Miao, C.W. Wang, and T.K. Lee · 2007
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Phase retrieval and saddle-point optimization
S. Marchesini · 2007
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A unified evaluation of iterative projection algorithms for phase retrieval
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Single particle X-ray diffractive imaging
M.J. Bogan and et al · 2008
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Enhancing sparsity by reweighted
E.J. Candès, M.B. Wakin, and S.P. Boyd · 2008
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Coherent detection in optical fiber systems
E. Ip, A.P.T. Lau, D.J.F. Barros, and J.M. Kahn · 2008
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Lectures on modern convex optimization: analysis, algorithms, and engineering applications
A. Ben-Tal and A. S. Nemirovski · 2001
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Painless reconstruction from magnitudes of frame coefficients
R. Balan, B. Bodmann, P.G. Casazza, and D. Edidin · 2009
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
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Templates for convex cone problems with applications to sparse signal recovery
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The Power of Convex Relaxation: Near-Optimal Matrix Completion
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Array imaging using intensity-only measurements
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Ptychographic x-ray computed tomography at the nanoscale
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Nanoscale imaging using deep ultraviolet digital holographic microscopy
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