Work

Sparse Signal Recovery Using Iterative Proximal Projection

Year: 2017

Type: article

Abstract: This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2017.2778695

Authors: Fateme Ghayem, Mostafa Sadeghi, Massoud Babaie-Zadeh, Saikat Chatterjee, Mikael Skoglund, Christian Jutten

Institutions: Sharif University of Technology, GIPSA-Lab, KTH Royal Institute of Technology, Institut Universitaire de France, Université Grenoble Alpes

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Iran National Science Foundation

OpenAlex ID: W2774820752

FWCI: 2.779

Cites: 50

Cited by: 44

Related to: 10