TY - JOUR
T1 - Enhanced Block-Sparse Signal Recovery Performance via Truncated $ℓ_2/ℓ_{1−2}$ Minimization
AU - Kong , Weichao
AU - Wang , Jianjun
AU - Wang , Wendong
AU - Zhang , Feng
JO - Journal of Computational Mathematics
VL - 3
SP - 437
EP - 451
PY - 2020
DA - 2020/03
SN - 38
DO - http://doi.org/10.4208/jcm.1811-m2017-0275
UR - https://global-sci.org/intro/article_detail/jcm/15794.html
KW - Compressed sensing, Block-sparse, Truncated $ℓ_2/ℓ_{1−2}$ minimization method, ADMM.
AB - <p style="text-align: justify;">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 ℓ<sub>1−2</sub> 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.</p>