Many imaging problems require solving an inverse problem that is ill-conditioned or ill-
posed. Imaging methods typically address this difficulty by regularizing the estimation …

We provide a complete framework for performing infinite dimensional Bayesian inference
and uncertainty quantification for image reconstruction with Poisson data. In particular, we …

The integration of sensing and communication has become essential to next-generation
vehicular networks. In this paper, we investigate a vehicle-to-infrastructure (V2I) network with …

This paper presents an empirical Bayesian method to estimate regularisation parameters in
imaging inverse problems. The method calibrates regularisation parameters directly from the …

This study presents a region‐based algorithm for segmenting colour texture image, which
uses Voronoi tessellation for partitioning the domain of the image and Markov random field …

A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden
Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods …

This paper presents a fast heuristic for comparing Bayesian models to solve inverse
problems related to signal processing. We focus on problems that are convex wrt the …

Dans le contexte de la surveillance spatiale, nous nous intéressons à un amas de débris
évoluant en orbite autour de la Terre et observé par un capteur radar. Il est alors constaté …

Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have
received an increasing interest due to the significant spectral information they convey about …

Many imaging problems require solving a high-dimensional inverse problem that is ill-
conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the …