Wavelet-based compressed sensing using a gaussian scale mixture model

Yookyung Kim, Mariappan S. Nadar, Ali Bilgin

Research output: Contribution to journalArticle

21 Scopus citations


While initial compressed sensing (CS) recovery techniques operated under the implicit assumption that the sparse domain coefficients are independently distributed, recent results have indicated that integrating a statistical or structural dependence model of sparse domain coefficients into CS enhances recovery. In this paper, we present a method for exploiting empirical dependences among wavelet coefficients during CS recovery using a Bayes least-square Gaussian-scale-mixture model. The proposed model is successfully incorporated into several recent CS algorithms, including reweighted l 1 minimization (RL1), iteratively reweighted least squares, and iterative hard thresholding. Extensive experiments including comparisons with a state-of-the-art model-based CS method demonstrate that the proposed algorithms are highly effective at reducing reconstruction error and/or the number of measurements required for a desired reconstruction quality.

Original languageEnglish (US)
Article number6156783
Pages (from-to)3102-3108
Number of pages7
JournalIEEE Transactions on Image Processing
Issue number6
StatePublished - Jun 1 2012


  • Compressed sensing (CS)
  • Gaussian scale mixtures (GSMs)
  • Structured sparsity

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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