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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…

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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…

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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…

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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…

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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…

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Jean-François Giovannelli — accepted AI-conference papers · AIConfPaper