71 citations · 276 across the 43 of their papers we have counts for
6 papers · 1 filter
Maximum entropy methods for texture synthesis: theory and practice
Valentin De Bortoli, Agnes Desolneux, Alain Durmus +2
Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formu…
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
Ana F. Vidal, Valentin De Bortoli, Marcelo Pereyra +1
Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation prob…
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra +1
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in p…
Patch redundancy in images: a statistical testing framework and some applications
De Bortoli Valentin, Desolneux Agnès, Galerne Bruno +1
In this work we introduce a statistical framework in order to analyze the spatial redundancy in natural images. This notion of spatial redundancy must be defined locally and thus w…
Macrocanonical Models for Texture Synthesis
De Bortoli Valentin, Desolneux Agnès, Galerne Bruno +1
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be ma…
Convergence of diffusions and their discretizations: from continuous to discrete processes and back
Valentin De Bortoli, Alain Durmus
In this paper, we establish new quantitative convergence bounds for a class of functional autoregressive models in weighted total variation metrics. To derive our results, we show…