Automated recovery of compressedly observed sparse signals from smooth background

Zhaofu Chen, Rafael Molina, Aggelos K. Katsaggelos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measurements in the presence of a smooth background component. This problem is closely related to robust principal component analysis and compressive sensing, and is found in a number of practical areas. The proposed algorithm adopts a hierarchical Bayesian framework for modeling, and employs approximate inference to estimate the unknowns. Numerical examples demonstrate the effectiveness of the proposed algorithm and its advantage over the current state-of-the-art solutions.

Original languageEnglish (US)
Article number6808512
Pages (from-to)1012-1016
Number of pages5
JournalIEEE Signal Processing Letters
Volume21
Issue number8
DOIs
StatePublished - Aug 2014

Keywords

  • Bayesian algorithm
  • compressive sensing
  • robust principal component analysis

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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