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Effective zero-norm minimization algorithms for noisy compressed sensing
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DOI：10.1016/j.jfranklin.2020.03.023.png)
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Abstract
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This paper proposes two new algorithms, namely (i) SSReL1Min(CVX)-Scalar-Sign function-based Reweighted L-1 - norm Minimization algorithm combined with Disciplined Convex Programming for a high-performance L-0 - norm Minimization algorithm and (ii) SSReL1Min(MBB) - SSReL1Min algo-rithm combined with modified Barzilai-Borwein algorithm for a computational fast L-0 - norm Mini-mization algorithm (without significantly sacrificing the performance). Based on the proposed L-0 - norm minimization algorithm, this paper also presents an upgraded compressed sensing to improve its performance on the recovery of noisy signals. The proposed L-0 - norm minimization algorithm includes a new optimal scalar-sign function-based weighting (in the least squares sense), as well as a new and system-atic mapping mechanism in pre-and post-processing, for noisy compressed sensing. This improvement is further confirmed by experimental results. Comparisons with different state-of-the-art solvers are also included, to show that the proposed method outperforms existing ones. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
SPARSE SIGNAL RECONSTRUCTION
RESTRICTED ISOMETRY PROPERTY
RECOVERY
DECOMPOSITION
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