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- W2603616603 abstract "The l 2,1-norm regularization can efficiently recover group-sparse signals whose non-zero coefficients occur in a few groups. It is well known that the l 2,1-norm regularization based on the classic alternating direction method shows strong stability and robustness in many applications. However, the l 2,1-norm regularization requires more measurements. In order to recover groupsparse signals with a better sparsity-measurement tradeoff, the truncated l 2,1-norm regularization and reweighted l 2,1-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 l 2,1-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." @default.
- W2603616603 created "2017-04-07" @default.
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- W2603616603 date "2017-02-20" @default.
- W2603616603 modified "2023-09-28" @default.
- W2603616603 title "Efficient recovery of group-sparse signals with truncated and reweighted l2, 1-regularization" @default.
- W2603616603 doi "https://doi.org/10.21629/jsee.2017.01.03" @default.
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