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Communication Dans Un Congrès Année : 2014

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

Résumé

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.
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Dates et versions

hal-01147247 , version 1 (30-04-2015)

Identifiants

  • HAL Id : hal-01147247 , version 1
  • OATAO : 12914

Citer

Lotfi Chaari, Hadj Batatia, Nicolas Dobigeon, Jean-Yves Tourneret. A hierarchical sparsity-smoothness Bayesian model for ℓ0 + ℓ1 + ℓ2 regularization. IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2014, May 2014, Florence, Italy. pp. 1901-1905. ⟨hal-01147247⟩
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