Name
Title | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2014 | Signal Processing Conference | 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,Langevin diffusion,MCMC methods,MMSE,Majorize-Minimize,multichannel image restoration |
DocType | ISSN | Citations |
Conference | 2076-1465 | 2 |
PageRank | References | Authors |
0.35 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Marnissi | 1 | 14 | 2.66 |
| Amel Benazza-Benyahia | 2 | 271 | 32.72 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| Jean-Christophe Pesquet | 4 | 206 | 22.24 |