Enhanced Block-Sparse Signal Recovery Performance via Truncated $ℓ_2/ℓ_{1−2}$ Minimization

Authors

  • Weichao Kong School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
  • Jianjun Wang School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
  • Wendong Wang School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
  • Feng Zhang School of Mathematics and Statistics, Southwest University, Chongqing 400715, China

DOI:

https://doi.org/10.4208/jcm.1811-m2017-0275

Keywords:

Compressed sensing, Block-sparse, Truncated $ℓ_2/ℓ_{1−2}$ minimization method, ADMM.

Abstract

In this paper, we investigate truncated $ℓ_2/ℓ_{1−2}$ minimization and its associated alternating direction method of multipliers (ADMM) algorithm for recovering the block sparse signals. Based on the block restricted isometry property (Block-RIP), a theoretical analysis is presented to guarantee the validity of proposed method. Our theoretical results not only show a less error upper bound, but also promote the former recovery condition of truncated ℓ1−2 method for sparse signal recovery. Besides, the algorithm has been compared with some state-of-the-art algorithms and numerical experiments have shown excellent performances on recovering the block sparse signals.

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Published

2020-03-24

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Issue

Vol. 38 No. 3 (2020)

Section

Articles

How to Cite

Enhanced Block-Sparse Signal Recovery Performance via Truncated $ℓ_2/ℓ_{1−2}$ Minimization. (2020). Journal of Computational Mathematics, 38(3), 437-451. https://doi.org/10.4208/jcm.1811-m2017-0275