Wavelet-based compressed sensing using a gaussian scale mixture model

Yookyung Kim, Mariappan S. Nadar, Ali Bilgin

Research output: Contribution to journalArticlepeer-review

23 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 2012


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

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Wavelet-based compressed sensing using a gaussian scale mixture model'. Together they form a unique fingerprint.

Cite this