109 citations · 367 across the 19 of their papers we have counts for
22 papers
Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling
Liam Moroy, Jean-François Giovannelli, Yoann Altmann +3
Training-free posterior sampling methods, also known as Plug-and-Play methods, leverage pretrained unconditional diffusion or flow-matching models to solve inverse problems. Most e…
Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
Jean-François Giovannelli
This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced…
A Gibbs posterior sampler for inverse problem based on prior diffusion model
Jean-François Giovannelli
This paper addresses the issue of inversion in cases where (1) the observation system is modeled by a linear transformation and additive error, (2) the problem is ill-posed and reg…
Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT
Liam Moroy, Guillaume Bourmaud, Frédéric Champagnat +1
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 s…
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior
Charlesquin Kemajou Mbakam, Jean-Francois Giovannelli, Marcelo Pereyra
Score-based diffusion methods provide a powerful strategy to solve image restoration tasks by flexibly combining a pre-trained foundational prior model with a likelihood function s…
A stochastic optimisation unadjusted Langevin method for empirical Bayesian estimation in semi-blind image deblurring problems
Charlesquin Kemajou Mbakam, Marcelo Pereyra, Jean-François Giovannelli
This paper presents a novel stochastic optimisation methodology to perform empirical Bayesian inference in semi-blind image deconvolution problems. Given a blurred image and a para…