1 citations · 2 across the 13 of their papers we have counts for
15 papers
Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction
Idris Tatachak, Luis Kabongo, Nicolas Papadakis +2
The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To th…
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…
On the Convergence of Proximal Algorithms for Weakly-convex Min-max Optimization
Guido Tapia-Riera, Camille Castera, Nicolas Papadakis
We study alternating first-order algorithms with no inner loops for solving nonconvex-strongly concave min-max problems. We show the convergence of the alternating gradient descent…
Reinterpreting EMML as Mirror Descent for Constrained Maximum Likelihood Estimation
Antonin Clerc, Ségolène Martin, Nicolas Papadakis +1
The Expectation--Maximization Maximum Likelihood (EMML) algorithm belongs to the Expectation--Maximization family and is widely used for image reconstruction problems under Poisson…
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…
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…
Optimization with First Order Algorithms
Charles Dossal, Samuel Hurault, Nicolas Papadakis
These notes focus on the minimization of convex functionals using first-order optimization methods, which are fundamental in many areas of applied mathematics and engineering. The…
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…
Diverse super-resolution with pretrained deep hiererarchical VAEs
Jean Prost, Antoine Houdard, Andrés Almansa +1
We investigate the problem of producing diverse solutions to an image super-resolution problem. From a probabilistic perspective, this can be done by sampling from the posterior di…