Enhanced Block-Sparse Signal Recovery Performance via Truncated $ℓ_2/ℓ_{1−2}$ Minimization (Journal of Computational Mathematics, 2020)
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 sparsesignals. Based on the block restricted isometry property (Block-RIP), a theoretical analysis is presented to guarantee the validity of proposed method. Our theoretical resultsnot only show a less error upper bound, but also promote the former recovery conditionof truncated ℓ1−2 method for sparse signal recovery. Besides, the algorithm has beencompared with some state-of-the-art algorithms and numerical experiments have shownexcellent performances on recovering the block sparse signals.
Keywords: Compressed sensing; Block-sparse; Truncated $ℓ_2/ℓ_{1−2}$ minimization method; ADMM.