1 citations · 1 across the 4 of their papers we have counts for
5 papers
Learning few-step posterior samplers by unfolding and distillation of diffusion models
Charlesquin Kemajou Mbakam, Jonathan Spence, Marcelo Pereyra
Diffusion models (DMs) have emerged as powerful image priors in Bayesian computational imaging. Two primary strategies have been proposed for leveraging DMs in this context: Plug-a…
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
Alessio Spagnoletti, Jean Prost, Andrés Almansa +2
Text-to-image latent diffusion models (LDMs) have recently emerged as powerful generative models with great potential for solving inverse problems in imaging. However, leveraging 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…
Do Bayesian imaging methods report trustworthy probabilities?
David Y. W. Thong, Charlesquin Kemajou Mbakam, Marcelo Pereyra
Bayesian statistics is a cornerstone of imaging sciences, underpinning many and varied approaches from Markov random fields to score-based denoising diffusion models. In addition t…
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…