Block-Sparse Recovery with Optimal Block Partition
preprintposted on 2022-03-03, 16:31 authored by Hiroki KurodaHiroki Kuroda, Daichi KitaharaDaichi Kitahara
This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.
Email Address of Submitting Authorkuroda@media.ritsumei.ac.jp
ORCID of Submitting Author0000-0002-8093-3219
Submitting Author's InstitutionRitsumeikan University
Submitting Author's Country