GESPAR: Efficient Phase Retrieval of Sparse Signals

• Published in: IEEE transactions on signal processing 2014-02, Vol.62 (4), p.928-938
• Main Authors: Shechtman, Yoav, Beck, Amir, Eldar, Yonina C.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Convergence; Correlation; Detection, estimation, filtering, equalization, prediction; Discrete Fourier transforms; Exact sciences and technology; Fourier transforms; Information, signal and communications theory; Non-convex optimization; phase retrieval; Signal and communications theory; Signal processing algorithms; Signal, noise; Sparse matrices; sparse signal processing; Telecommunications and information theory; Vectors
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2013.2297687

We consider the problem of phase retrieval, namely, recovery of a signal from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of Fourier phase information, this problem is ill-posed. Therefore, prior information on the signal is needed in order to enable its recovery. In this work we consider the case in which the signal is known to be sparse, i.e., it consists of a small number of nonzero elements in an appropriate basis. We propose a fast local search method for recovering a sparse signal from measurements of its Fourier transform (or other linear transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse Retrieval. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that GESPAR is fast and more accurate than existing techniques in a variety of settings.