$$\ell _{1/2, 1}$$ℓ1/2, 1 group sparse regularization for compressive sensing. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Liu, Shengcai; Zhang, Jiangshe; Liu, Junmin; Yin, Qingyan
• Is Part Of: Signal, image and video processing. Volume 10:Issue 5(2016)
• Page Start: 861
• Page End: 868
• Publication Date: 2016-07
• Subjects: Compressive sensing -- Group sparsity -- Regularization -- Null space property; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0829-6
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Recently, the design of group sparse regularization has drawn much attention in group sparse signal recovery problem. Two of the most popular group sparsity-inducing regularization models are $$\ell _{1, 2}$$ ℓ 1, 2 and $$\ell _{1, \infty }$$ ℓ 1, ∞ regularization. Nevertheless, they do not promote the intra-group sparsity. For example, Huang and Zhang (Ann Stat 38:1978–2004, 2010) claimed that the $$\ell _{1, 2}$$ ℓ 1, 2 regularization is superior to the $$\ell _1$$ ℓ 1 regularization only for strongly group sparse signals. This means the sparsity of intra-group is useless for $$\ell _{1, 2}$$ ℓ 1, 2 regularization. Our experiments show that recovering signals with intra-group sparse needs more measurements than those without, by the $$\ell _{1, \infty }$$ ℓ 1, ∞ regularization. In this paper, we propose a novel group sparsity-inducing regularization defined as a mixture of the $$\ell _{1/2}$$ ℓ 1 / 2 norm and the $$\ell _{1}$$ ℓ 1 norm, referred to as $$\ell _{1/2, 1}$$ ℓ 1 / 2, 1 regularization, which can overcome these shortcomings of $$\ell _{1, 2}$$ ℓ 1, 2 and $$\ell _{1, \infty }$$ ℓ 1, ∞ regularization. We define a new null space property for $$\ell _{1/2, 1}$$ ℓ 1 / 2, 1 regularization and apply it to establish a recoverability theory for both intra-group and inter-group sparse signals. In addition, we introduce an iteratively reweighted algorithm to solve this model and analyze its convergence. Comprehensive experiments on simulated data show that the proposed $$\ell _{1/2, 1}$$ ℓ 1 / 2, 1 regularization is superior to $$\ell _{1, 2}$$ ℓ 1, 2 and $$\ell _{1, \infty }$$ ℓ 1, ∞ regularization.