8 citations · 9 across the 5 of their papers we have counts for
5 papers
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…
Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation
Cecilia Tarpau, Ming Fang, Konstantinos C. Zygalakis +3
This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign,…
Proximal nested sampling with data-driven priors for physical scientists
Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price +2
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for mode…
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
Alain Durmus, Eric Moulines, Marcelo Pereyra
Modern imaging methods rely strongly on Bayesian inference techniques to solve challenging imaging problems. Currently, the predominant Bayesian computation approach is convex opti…