19 citations · 40 across the 12 of their papers we have counts for
13 papers
Convergent Plug-and-Play Image Restoration with Annealed Noise Levels
Samuel Hurault
Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical implementations often decrease…
Geometry-Aware Discretization Error of Diffusion Models
Samuel Hurault, Thomas Moreau, Gabriel Peyré
Practical diffusion sampling requires simulating a reverse-time ODE or SDE with a limited number of denoising steps, making the choice of sampling parameters crucial for minimizing…
Tessellations of Semi-Discrete Flow Matching
Emile Pierret, Johannes Hertrich, Samuel Hurault +1
We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. This semi-discrete regime is th…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
Samuel Hurault, Matthieu Terris, Thomas Moreau +1
Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current state-of-the-art methods follow…
Reconstruct Anything Model: a lightweight general model for computational imaging
Matthieu Terris, Samuel Hurault, Maxime Song +1
Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion 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…
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…
Self-Consistent Velocity Matching of Probability Flows
Lingxiao Li, Samuel Hurault, Justin Solomon
We present a discretization-free scalable framework for solving a large class of mass-conserving partial differential equations (PDEs), including the time-dependent Fokker-Planck e…
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…
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…