期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 66, 期 4, 页码 879-894出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2017.2778695
关键词
Sparse signal recovery; compressed sensing; SL0; proximal splitting algorithms; iterative sparsification-projection
资金
- Center for International Scientific Studies and Collaboration
- Iran National Science Foundation
- [ERC-2012AdG-320684-CHESS]
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.
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