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
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