50 citations · 112 across the 11 of their papers we have counts for
12 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…
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction
Pierre-Antoine Comby, Benjamin Lapostolle, Matthieu Terris +1
Achieving high-quality Magnetic Resonance Imaging (MRI) reconstruction at accelerated acquisition rates remains challenging due to the inherent ill-posed nature of the inverse prob…
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…
The AIRI plug-and-play algorithm for image reconstruction in radio-interferometry: variations and robustness
Matthieu Terris, Chao Tang, Adrian Jackson +1
Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) denoiser behaving as…
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…