Majorize-Minimize adapted Metropolis-Hastings algorithm. Application to multichannel image recovery

Yosra Marnissi 1 Amel Benazza-Benyahia 2 Emilie Chouzenoux 1 Jean-Christophe Pesquet 1
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 COSIM-Laboratoire Communication, Signaux et Images
Abstract : One challenging task in MCMC methods is the choice of the proposal density. It should ideally provide an accurate approximation of the target density with a low computational cost. In this paper, we are interested in Langevin diffusion where the proposal accounts for a directional component. We propose a novel method for tuning the related drift term. This term is preconditioned by an adaptive matrix based on a Majorize-Minimize strategy. This new procedure is shown to exhibit a good performance in a multispectral image restoration example.
Keywords : multichannel image restoration MMSE Majorize-Minimize Langevin diffusion MCMC methods
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Communication dans un congrès
22th European Signal Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. 2014
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Yosra Marnissi, Amel Benazza-Benyahia, Emilie Chouzenoux, Jean-Christophe Pesquet. Majorize-Minimize adapted Metropolis-Hastings algorithm. Application to multichannel image recovery. 22th European Signal Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. 2014. <hal-01077273>

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