Paper Info
Title
A nonconvex regularized approach for phase retrieval
Abstract
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.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025351
Image Processing
Keywords
Field
DocType
approximation theory,image reconstruction,image retrieval,optimisation,block coordinate proximal algorithm,computational cost,convex separable regularization functions,estimation performance,image reconstruction problem,imaging systems,large-scale problems,large-size data sets,nonFourier measurements,nonconvex formulation,nonconvex regularized approach,optimization process,phase retrieval,smooth nonconvex approximation,standard data fidelity,Nonconvex optimization,Nonsmooth optimization,Phase retrieval problem,Proximal methods
Iterative reconstruction,Mathematical optimization,Fidelity,Data set,Phase retrieval,Computer science,Separable space,Regular polygon,Regularization (mathematics)
Conference
ISSN
Citations 
PageRank 
1522-4880
4
0.44
References 
Authors
16
3
Name
Order
Citations
PageRank
Audrey Repetti1766.84
Emilie Chouzenoux220226.37
Jean-Christophe Pesquet320622.24