Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853929 | Acoustics, Speech and Signal Processing |
Keywords | DocType | Citations |
Bayes methods,Gaussian distribution,image restoration,smoothing methods,Bernoulli-generalized Gauss-Laplace distribution,appropriate prior,hierarchical sparsity-smoothness Bayesian model,l0+l1+ l2 regularization,signal restoration,signal smoothing,sparse image recovery,sparse signal recovery,target image sparsity,target signal sparsity,MCMC,hierarchical Bayesian models,restoration,smoothness,sparsity | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lotfi Chaâri | 1 | 111 | 13.30 |
| Hadj Batatia | 2 | 0 | 0.34 |
| Nicolas Dobigeon | 3 | 2070 | 108.02 |
| Jean-Yves Tourneret | 4 | 835 | 64.32 |