DocumentCode :
148892
Title :
Majorize-Minimize adapted metropolis-hastings algorithm. Application to multichannel image recovery
Author :
Marnissi, Y. ; Benazza-Benyahia, A. ; Chouzenoux, Emilie ; Pesquet, J.-C.
Author_Institution :
LIGM, Univ. Paris-Est, Champs-sur-Marne, France
fYear :
2014
fDate :
1-5 Sept. 2014
Firstpage :
1332
Lastpage :
1336
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 :
Markov processes; Monte Carlo methods; image restoration; matrix algebra; Langevin diffusion; MCMC method; Markov chain Monte Carlo approach; adaptive matrix; computational cost; directional component; majorize-minimize adapted metropolis-hastings algorithm; multichannel image recovery; multispectral image restoration example; proposal density; target density; Covariance matrices; Image restoration; Markov processes; Monte Carlo methods; Proposals; Signal to noise ratio; Vectors; Langevin diffusion; MCMC methods; MMSE; Majorize-Minimize; multichannel image restoration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
Type :
conf
Filename :
6952466
Link To Document :
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