Efficient recovery of group-sparse signals with truncated and reweighted l_(2,1)-regularization  

作　　者：Yan Zhang Jichang Guo Xianguo Li 

机构地区：[1]School of Electronic Information Engineering, Tianjin University [2]School of Computer and Information Engineering, Tianjin Chengjian University [3]School of Electronic and Information Engineering, Tianjin Polytechnic University

出　　处：《Journal of Systems Engineering and Electronics》2017年第1期19-26,共8页系统工程与电子技术（英文版）

基　　金：supported by the Research Fund for the Doctoral Program of Higher Education of China(20120032110034)

摘　　要：The l2, 1-norm regularization can efficiently recover group-sparse signals whose non-zero coefficients occur in a few groups. It is well known that the l2, 1-norm regularization based on the classic alternating direction method shows strong stability and robustness in many applications. However, the l2, 1-norm regularization requires more measurements. In order to recover group-sparse signals with a better sparsity-measurement tradeoff, the truncated l2, 1-norm regularization and reweighted l2, 1-norm regularization are proposed for the recovery of group-sparse signals based on the iterative support detection. The proposed algorithms are tested and compared with the l2, 1-norm model on a series of synthetic signals and the Shepp-Logan phantom. Experimental results demonstrate the performance of the proposed algorithms, especially at a low sample rate and high sparsity level. © 1990-2011 Beijing Institute of Aerospace Information.The l2, 1-norm regularization can efficiently recover group-sparse signals whose non-zero coefficients occur in a few groups. It is well known that the l2, 1-norm regularization based on the classic alternating direction method shows strong stability and robustness in many applications. However, the l2, 1-norm regularization requires more measurements. In order to recover group-sparse signals with a better sparsity-measurement tradeoff, the truncated l2, 1-norm regularization and reweighted l2, 1-norm regularization are proposed for the recovery of group-sparse signals based on the iterative support detection. The proposed algorithms are tested and compared with the l2, 1-norm model on a series of synthetic signals and the Shepp-Logan phantom. Experimental results demonstrate the performance of the proposed algorithms, especially at a low sample rate and high sparsity level. © 1990-2011 Beijing Institute of Aerospace Information.

关 键 词：Iterative methods RECOVERY 

分 类 号：TN911.7[电子电信—通信与信息系统]