• 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