Effective zero-norm minimization algorithms for noisy compressed sensing

Shu Mei Guo, Chen Kai Huang, Tzu Jui Huang, Jason Sheng Hong Tsai, Leang San Shieh, Jose I. Canelon

Research output: Contribution to journalArticlepeer-review


This paper proposes two new algorithms, namely (i) SSReL1Min(CVX)-Scalar-Sign function-based Reweighted L1−norm Minimization algorithm combined with Disciplined Convex Programming for a high-performance L0−norm Minimization algorithm and (ii) SSReL1Min(MBB) – SSReL1Min algorithm combined with modified Barzilai-Borwein algorithm for a computational fast L0−norm Minimization algorithm (without significantly sacrificing the performance). Based on the proposed L0−norm minimization algorithm, this paper also presents an upgraded compressed sensing to improve its performance on the recovery of noisy signals. The proposed L0−norm minimization algorithm includes a new optimal scalar-sign function-based weighting (in the least squares sense), as well as a new and systematic 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.

Original languageEnglish
Pages (from-to)7159-7187
Number of pages29
JournalJournal of the Franklin Institute
Issue number11
Publication statusPublished - 2020 Jul

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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