An Optimization-Oriented Algorithm for Sparse Signal Reconstruction

Fulin Li, Shaohua Hong, Yujie Gu, Lin Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.

Original languageEnglish
Article number8633941
Pages (from-to)515-519
Number of pages5
JournalIEEE Signal Processing Letters
Issue number3
StatePublished - Mar 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Compressive sensing
  • global optimization
  • greedy algorithms
  • optimization-oriented algorithm


Dive into the research topics of 'An Optimization-Oriented Algorithm for Sparse Signal Reconstruction'. Together they form a unique fingerprint.

Cite this