Enhanced sampling schemes for MCMC based blind Bernoulli-Gaussian deconvolution

Rééchantillonnage de l’échelle dans les algorithmes MCMC pour les problèmes inverses bilinéaires

This article presents an efficient method for improving the behavior of the MCMC sampling algorithm involved in the resolution of bilinear inverse problems. Blind deconvolution and source separation are among the applications that benefit from this improvement. The proposed method addresses the scale ambiguity inherent to bilinear inverse problems. Solving this type of problem within a Bayesian...

Maximum-likelihood blind deconvolution: non-white Bernoulli-Gaussian case

A Fast Blind Impulse Detector for Bernoulli-Gaussian Noise in Underspread Channel

The Bernoulli-Gaussian (BG) model is practical to characterize impulsive noises that widely exist in various communication systems. To estimate the BG model parameters from noise measurements, a precise impulse detection is essential. In this paper, we propose a novel blind impulse detector, which is proven to be fast and accurate for BG noise in underspread communication channels.

Image deconvolution using a stochastic differential equation approach

ABSTRACT We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classically optimized by a MCMC sampler embedded into a simulated annealing scheme. In a previous work, we have shown that, in the context of image denoising, a diffusion process can outperform the MCMC approach in ...

Déconvolution impulsionnelle positive myope

We consider in this paper the problem of the blind deconvolution of positive peaks, i.e. we wish to restore a sparse spike train whose peaks are positive and to estimate the impulse response and the problem hyperparameters. Because of the complexity of the posterior global criterion, we use an MCMC method based on the Gibbs sampler. Finally, we propose two new indexes to give an estimate of the...