A hierarchical sparsity-smoothness Bayesian model for ℓ0 + ℓ1 + ℓ2 regularization

Lotfi Chaari, Hadj Batatia, Nicolas Dobigeon, Jean-Yves Tourneret

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

12 Citations (Scopus)

Abstract

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades. To address the ill-posedness of the related inverse problem, regularization is often essential by using appropriate priors that promote the sparsity of the target signal/image. In this context, ℓ0 + ℓ1 regularization has been widely investigated. In this paper, we introduce a new prior accounting simultaneously for both sparsity and smoothness of restored signals. We use a Bernoulli-generalized Gauss-Laplace distribution to perform ℓ0 + ℓ1 + ℓ2 regularization in a Bayesian framework. Our results show the potential of the proposed approach especially in restoring the non-zero coefficients of the signal/image of interest.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1901-1905
Number of pages5
ISBN (Electronic)9781479928934
DOIs
Publication statusPublished - 14 Jul 2014
Event39th IEEE International Conference on Acoustics, Speech and Signal Processing 2014 - Florence, Italy, Florence, Italy
Duration: 4 May 20149 May 2014
http://www.icassp2014.org/home.html

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference39th IEEE International Conference on Acoustics, Speech and Signal Processing 2014
Abbreviated titleICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14
OtherICASSP is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The series is sponsored by the IEEE Signal Processing Society and has been held annually since 1976. The conference features world-class speakers, tutorials, exhibits, a Show and Tell event, and over 120 lecture and poster sessions.
ICASSP is a cooperative effort of the IEEE Signal Processing Society Technical Committees:
Audio and Acoustic Signal Processing
Bio Imaging and Signal Processing
Design and Implementation of Signal Processing Systems
Image, Video, and Multidimenional Signal Processing
Information Forensics and Security
Industry DSP Technology Standing Committee
Machine Learning for Signal Processing
Multimedia Signal Processing
Sensor Array and Multichannel
Signal Processing for Communications and Networking
Signal Processing Education Standing Committee
Signal Processing Theory and Methods
Speech and Langauge Processing
Internet address

Keywords

  • hierarchical Bayesian models
  • MCMC
  • restoration
  • smoothness
  • sparsity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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