21 citations · 92 across the 18 of their papers we have counts for
21 papers
Controllable blind deblurring with diffusion models
Imane Si Salah, Emile Cribelier, Thomas Veit +2
Image acquisition with a camera involves several degradations due to the optical system, sensor, or low-level processing steps. We address blind deblurring in professional photogra…
Equivariant Denoisers for Plug and Play Image Restoration
Marien Renaud, Eliot Guez, Arthur Leclaire +1
One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods…
From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
Vincent Herfeld, Baudouin Denis de Senneville, Arthur Leclaire +1
In this paper we analyze the Gradient-Step Denoiser and its usage in Plug-and-Play algorithms. The Plug-and-Play paradigm of optimization algorithms uses off the shelf denoisers to…
On the Moreau envelope properties of weakly convex functions
Marien Renaud, Arthur Leclaire, Nicolas Papadakis
In this document, we present the main properties satisfied by the Moreau envelope of weakly convex functions. The Moreau envelope has been introduced in convex optimization to regu…
From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling
Marien Renaud, Valentin De Bortoli, Arthur Leclaire +1
We consider the problem of sampling distributions stemming from non-convex potentials with Unadjusted Langevin Algorithm (ULA). We prove the stability of the discrete-time ULA to d…
Equivariant Denoisers for Image Restoration
Marien Renaud, Arthur Leclaire, Nicolas Papadakis
One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods…
Plug-and-Play image restoration with Stochastic deNOising REgularization
Marien Renaud, Jean Prost, Arthur Leclaire +1
Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. E…
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter
Samuel Hurault, Antonin Chambolle, Arthur Leclaire +1
In this work, we present new proofs of convergence for Plug-and-Play (PnP) algorithms. PnP methods are efficient iterative algorithms for solving image inverse problems where regul…
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems
Samuel Hurault, Ulugbek Kamilov, Arthur Leclaire +1
Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of th…
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser
Samuel Hurault, Antonin Chambolle, Arthur Leclaire +1
This paper presents a new convergent Plug-and-Play (PnP) algorithm. PnP methods are efficient iterative algorithms for solving image inverse problems formulated as the minimization…
On the potential benefits of entropic regularization for smoothing Wasserstein estimators
Jérémie Bigot, Paul Freulon, Boris P. Hejblum +1
This paper is focused on the study of entropic regularization in optimal transport as a smoothing method for Wasserstein estimators, through the prism of the classical tradeoff bet…
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault, Arthur Leclaire, Nicolas Papadakis
Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative proximal algorithms by replacing a proximal operator by a denoising operation. When applied with deep…
Gradient Step Denoiser for convergent Plug-and-Play
Samuel Hurault, Arthur Leclaire, Nicolas Papadakis
Plug-and-Play methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods…
On the Existence of Optimal Transport Gradient for Learning Generative Models
Antoine Houdard, Arthur Leclaire, Nicolas Papadakis +1
The use of optimal transport cost for learning generative models has become popular with Wasserstein Generative Adversarial Networks (WGAN). Training of WGAN relies on a theoretica…
An algorithm for non-convex off-the-grid sparse spike estimation with a minimum separation constraint
Yann Traonmilin, Jean-François Aujol, Arhur Leclaire
Theoretical results show that sparse off-the-grid spikes can be estimated from (possibly compressive) Fourier measurements under a minimum separation assumption. We propose a pract…
The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension
Yann Traonmilin, Jean-François Aujol, Arthur Leclaire
Non-convex methods for linear inverse problems with low-dimensional models have emerged as an alternative to convex techniques. We propose a theoretical framework where both finite…
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
Antoine Houdard, Arthur Leclaire, Nicolas Papadakis +1
We propose GOTEX, a general framework for texture synthesis by optimization that constrains the statistical distribution of local features. While our model encompasses several exis…
Projected gradient descent for non-convex sparse spike estimation
Yann Traonmilin, Jean-François Aujol, Arthur Leclaire
We propose a new algorithm for sparse spike estimation from Fourier measurements. Based on theoretical results on non-convex optimization techniques for off-the-grid sparse spike e…
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…
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…