1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems
Matthieu Terris, Ulugbek S. Kamilov, Thomas Moreau
Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on d…
Equivariant plug-and-play image reconstruction
Matthieu Terris, Thomas Moreau, Nelly Pustelnik +1
Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser. These algorithms…
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers
Matthieu Terris, Thomas Moreau
Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapp…