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

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.

Mots clés: MCMC methods; Langevin diffusion; Majorize-Minimize; MMSE; multichannel image restoration

Domaines: Traitement du signal et de l'image [eess.SP]

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. ⟨hal-01077273⟩