Systems Engineering and Electronics

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Efficient recovery of group-sparse signals with truncated and reweighted l2,1-regularization

Yan Zhang1,2, Jichang Guo1,*, and Xianguo Li3   

  1. 1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;
    2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China;
    3. School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • Online:2017-02-24 Published:2010-01-03


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 groupsparse 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 seriesof 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.