Robust sparse representation has shown significant potential in solving challenging
problems in computer vision such as biometrics and visual surveillance. Although several …

The quantitative validation of reconstruction algorithms requires reliable data. Rasterized
simulations are popular but they are tainted by an aliasing component that impacts the …

In this work, we exploit the fact that wavelets can represent magnetic resonance images
well, with relatively few coefficients. We use this property to improve magnetic resonance …

In a previous work, an attempt was made to give a unified view of some acoustic holographic
methods within a Bayesian framework. One advantage of the so-called “Bayesian Focusing” …

A popular way to restore images comprising edges is to minimize a cost function combining
a quadratic data-fidelity term and an edge-preserving (possibly nonconvex) regularization …

Conditional random fields (CRFs) are popular discriminative models for computer vision and
have been successfully applied in the domain of image restoration, especially to image …

The paper addresses the generalization of the half-quadratic minimization method for the
restoration of images having values in a complete Riemannian manifold. We recall the half …

We address the problem of estimating spectral lines from irregularly sampled data within the
framework of sparse representations. Spectral analysis is formulated as a linear inverse …

The resolution of many large-scale inverse problems using MCMC methods requires a step
of drawing samples from a high dimensional Gaussian distribution. While direct Gaussian …

Variational Bayesian approximations have been widely used in fully Bayesian inference for
approximating an intractable posterior distribution by a separable one. Nevertheless, the …