HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download

Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Friday, October 24, 2014 - 12:11:10 PM
Last modification on : Saturday, January 15, 2022 - 3:56:51 AM
Long-term archiving on: : Sunday, January 25, 2015 - 10:20:15 AM


Files produced by the author(s)


  • HAL Id : hal-01077273, version 1


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⟩



Record views


Files downloads