Sparse Bayesian image restoration with linear operator uncertainties with application to EEG signal recovery

Lotfi Chaari, Hadj Batatia, Jean-Yves Tourneret

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades, especially in the biomedical field. Many techniques generally try to regularize the considered ill-posed inverse problem by defining appropriate priors for the target signal/image. The target regularization problem can then be solved either in a variational or Bayesian context. However, a little interest has been devoted to the uncertainties about the linear operator, which can drastically alter the reconstruction quality. In this paper, we propose a novel method for signal/image recovery that accounts and corrects the linear operator imprecisions. The proposed approach relies on a Bayesian formulation which is applied to EEG signal recovery. Our results show the promising potential of the proposed method compared to other regularization techniques which do not account for any error affecting the linear operator.

Original languageEnglish
Title of host publication2nd Middle East Conference on Biomedical Engineering
PublisherIEEE
Pages139-142
Number of pages4
ISBN (Electronic)9781479947997
DOIs
Publication statusPublished - 7 Apr 2014
Event2nd Middle East Conference on Biomedical Engineering 2014 - Doha, Qatar
Duration: 17 Feb 201420 Feb 2014

Publication series

NameMiddle East Conference on Biomedical Engineering
ISSN (Print)0018-9294
ISSN (Electronic)1558-2531

Conference

Conference2nd Middle East Conference on Biomedical Engineering 2014
Abbreviated titleMECBME 2014
CountryQatar
CityDoha
Period17/02/1420/02/14

Keywords

  • EEG/MEG
  • Hierarchical Bayesian Model
  • linear operator
  • MCMC
  • Sparse restoration

ASJC Scopus subject areas

  • Biomedical Engineering

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