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• W2166570128 abstract "Sparse signal recovery has been widely investigated during the last decade. This issue arises especially in large data applications where regularization is essential to recover a stable solution of the related inverse problem. Bayesian and variational regularization methods rely on different assumptions and lead to different estimators. Although variational methods are fast and efficient in convex problems, Bayesian methods are very robust and efficient mainly due to their ability to automatically estimate regularization parameters. This is usually performed using Markov Chain Monte Carlo (MCMC) algorithms to sample according to the target posteriors. Hamiltonian dynamics is one of the emergent sampling techniques. It is founded on a particular strategy to propose candidates which involves the gradient of the neg-logarithm of the target distribution. For sparse regularization problems, the neg-logarithm of the posteriors is generally non-differentiable. In this paper, we address this problem by resorting to proximal operators, a technique that has been widely used in the variational regularization literature. The resulting prox-MC method is tested and validated on a number of experiments and results show its ability in accurately recovering sparse signals/images." @default.
• W2166570128 created "2016-06-24" @default.
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• W2166570128 date "2014-01-16" @default.
• W2166570128 modified "2023-09-27" @default.
• W2166570128 title "Sparse Signal and Image Recovery Using A Proximal Bayesian Algorithm" @default.
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• W2166570128 hasPublicationYear "2014" @default.
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