Sparsity averaging for compressive imaging

Rafael E Carrillo, Jason D McEwen, Dimitri Van De Ville, Jean-Philippe Thiran, Yves Wiaux

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

Abstract

We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted ℓ 1 formulation, through extensive numerical simulations on natural images for spread spectrum and random Gaussian acquisition schemes. Our results show that average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity. Code and test data are available at https://github.com/basp-group/sopt. © 1994-2012 IEEE.

Original languageEnglish
Article number6507650
Pages (from-to)591-594
Number of pages4
JournalIEEE Signal Processing Letters
Volume20
Issue number6
DOIs
Publication statusPublished - 20 May 2013

Keywords

  • Compressed sensing
  • sparse approximation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics

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