Cosamp and SP for the cosparse analysis model

Raja Giryes*, Michael Elad

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


CoSaMP and Subspace-Pursuit (SP) are two recovery algorithms that find the sparsest representation for a given signal under a given dictionary in the presence of noise. These two methods were conceived in the context of the synthesis sparse representation modeling. The cosparse analysis model is a recent construction that stands as an interesting alternative to the synthesis approach. This new model characterizes signals by the space they are orthogonal to. Despite the similarity between the two, the cosparse analysis model is markedly different from the synthesis one. In this paper we propose analysis versions of the CoSaMP and the SP algorithms, and demonstrate their performance for the compressed sensing problem.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Number of pages5
StatePublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference20th European Signal Processing Conference, EUSIPCO 2012


  • Analysis
  • CoSaMP
  • Compressed Sensing
  • Sparse representations
  • Subspace-Pursuit
  • Synthesis


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