1 citations · 1 across the 10 of their papers we have counts for
11 papers
i-DEQ: A stable inertial deep equilibrium model for image restoration
Antonin Clerc, Marien Renaud, Baudouin Denis De Seneville +2
Deep Equilibrium Models (DEQs) are an established framework for image restoration that learn a problem-adapted regularization by solving a fixed-point (i.e. equilibrium) problem. W…
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…
Provably Accelerated Imaging with Restarted Inertia and Score-based Image Priors
Marien Renaud, Julien Hermant, Deliang Wei +1
Fast convergence and high-quality image recovery are two essential features of algorithms for solving ill-posed imaging inverse problems. Existing methods, such as regularization b…
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…
Audio signal interpolation using optimal transportation of spectrograms
David Valdivia, Marien Renaud, Elsa Cazelles +1
We present a novel approach for generating an artificial audio signal that interpolates between given source and target sounds. Our approach relies on the computation of Wasserstei…
Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm
Marien Renaud, Julien Hermant, Nicolas Papadakis
Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to recover a clean image from a degraded observation. These algorithms are kno…
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…
Gradient correlation is a key ingredient to accelerate SGD with momentum
Julien Hermant, Marien Renaud, Jean-François Aujol +2
Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pe…
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…
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
Marien Renaud, Jiaming Liu, Valentin de Bortoli +2
Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emer…