6 papers
EquivAnIA: A Spectral Method for Rotation-Equivariant Anisotropic Image Analysis
Jérémy Scanvic, Nils Laurent
Anisotropic image analysis is ubiquitous in medical and scientific imaging, and while the literature on the subject is extensive, the robustness to numerical rotations of numerous…
Equivariant Splitting: Self-supervised learning from incomplete data
Victor Sechaud, Jérémy Scanvic, Quentin Barthélemy +2
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learnin…
UNet-AF: An alias-free UNet for image restoration
Jérémy Scanvic, Quentin Barthélemy, Julián Tachella
The simplicity and effectiveness of the UNet architecture makes it ubiquitous in image restoration, image segmentation, and diffusion models. They are often assumed to be equivaria…
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Jérémy Scanvic, Mike Davies, Patrice Abry +1
Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scien…
Translation-Equivariance of Normalization Layers and Aliasing in Convolutional Neural Networks
Jérémy Scanvic, Quentin Barthélemy, Julián Tachella
The design of convolutional neural architectures that are exactly equivariant to continuous translations is an active field of research. It promises to benefit scientific computing…
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…