@inproceedings{Petros2018,
abstract = {In this paper we address the Phase Retrieval problem, which aims to recover the phase of the Fourier transform of a signal when only magnitude measurements are available. Following recent developments in Phase Retrieval, the problem can be transformed into a convex semidefinite programming optimization problem, which can be solved using Matrix Completion techniques. In this paper the acquisition process is modeled using a likelihood function, which splits the original problem into two convex optimization problems, and alternates between the solution of each of them. To relate both convex problems we introduce a heuristic, which results in fast convergence of the proposed method.},
author = {Nyfantis, Petros and Ruiz, Pablo and Katsaggelos, Aggelos K.},
booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2018.8451272},
isbn = {978-1-4799-7061-2},
issn = {15224880},
keywords = {Alternating minimization,Lifting,Nonconvex quadratic programming,Phase,Retrieval},
month = {oct},
pages = {3983--3987},
publisher = {IEEE},
title = {{Probabilistic Matrix Completion for Image Phase Retrieval}},
url = {https://ieeexplore.ieee.org/document/8451272/},
year = {2018}
}

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