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Jean-François Giovannelli

5 accepted papers

2025

Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT

ICASSP 2025accepted

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently…

Cited by 0SourceScholar
2017

A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images

ICASSP 2017accepted

A generalized Swendsen-Wang (GSW) algorithm is proposed for the joint segmentation of a set of multiple images sharing, in part, an unknown number of common classes. The class labels are a priori modeled by a combination of the hierarchical Dirichlet process (HDP) and the Potts model. The HDP allows…

Cited by 0SourceScholar
2017

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling

NeurIPS 2017poster

We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution. Similarly to Hogwild methods, our approach does not target the original Gaussian distribution of interest, but an approximation to it. Contrary to Hogwild m…

Cited by 12SourcePDFScholar
2015

Gradient scan Gibbs sampler: An efficient high-dimensional sampler application in inverse problems

ICASSP 2015accepted

The paper deals with Gibbs samplers that include high-dimensional conditional Gaussian distributions. It proposes an efficient algorithm that only requires a scalar Gaussian sampling. The algorithm relies on a random excursion along a random direction. It is proved to converge, i.e. the drawn sample…

Cited by 0SourceScholar
2015

Potts model parameter estimation in Bayesian segmentation of piecewise constant images

ICASSP 2015accepted

The paper presents a method for estimating the parameter of a Potts model jointly with the unknowns of an image segmentation problem. The method addresses piecewise constant images degraded by additive noise. The proposed solution follows a Bayesian approach, that yields the posterior law for all th…

Cited by 0SourceScholar