Sparse Bayesian regularization using Bernoulli-Laplacian priors

Lotfi Chaari, Jean-Yves Tourneret, Hadj Batatia

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

22 Citations (Scopus)


Sparse regularization has been receiving an increasing interest in the literature. Two main difficulties are encountered when performing sparse regularization. The first one is how to fix the parameters involved in the regularization algorithm. The second one is to optimize the inherent cost function that is generally non differentiable, and may also be non-convex if one uses for instance an ℓ0 penalization. In this paper, we handle these two problems jointly and propose a novel algorithm for sparse Bayesian regularization. An interesting property of this algorithm is the possibility of estimating the regularization parameters from the data. Simulation performed with 1D and 2D restoration problems show the very promising potential of the proposed approach. An application to the reconstruction of electroencephalographic signals is finally investigated.

Original languageEnglish
Title of host publication21st European Signal Processing Conference (EUSIPCO 2013)
ISBN (Electronic)9780992862602
Publication statusPublished - 8 May 2014
Event21st European Signal Processing Conference 2013 - Morocco, Marrakech, Morocco
Duration: 9 Sept 201313 Sept 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


Conference21st European Signal Processing Conference 2013
Abbreviated titleEUSIPCO 2013


  • MCMC methods
  • parameter estimation
  • Sparse Bayesian restoration

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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