7 papers
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…
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
Alessio Spagnoletti, Jean Prost, Andrés Almansa +2
Text-to-image latent diffusion models (LDMs) have recently emerged as powerful generative models with great potential for solving inverse problems in imaging. However, leveraging s…
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…