50 citations · 112 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
Matthieu Terris, Arwa Dabbech, Chao Tang +1
We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play…