A Nonconvex Regularized Approach for Phase Retrieval

Audrey Repetti, Emilie Chouzenoux, Jean-Christophe Pesquet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)


With the development of new imaging systems delivering large-size data sets, phase retrieval has become recently the focus of much attention. The problem is especially challenging due to its intrinsically nonconvex formulation. In addition, the applicability of many existing solutions may be limited either by their estimation performance or by their computational cost, especially in the case of non-Fourier measurements. In this paper, we propose a novel phase retrieval approach, which is based on a smooth nonconvex approximation of the standard data fidelity term. In addition, the proposed method allows us to employ a wide range of convex separable regularization functions. The optimization process is performed by a block coordinate proximal algorithm which is amenable to solving large-scale problems. An application of this algorithm to an image reconstruction problem shows that it may be very competitive with respect to state-of-the-art methods.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 29 Jan 2015
Event21st IEEE International Conference on Image Processing 2014 - Paris, France
Duration: 27 Oct 201430 Oct 2014


Conference21st IEEE International Conference on Image Processing 2014
Abbreviated titleICIP 2014


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