16 papers
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…
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…
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
Brayan Monroy, Jorge Bacca, Julián Tachella
Self-supervised image denoising methods have traditionally relied on either architectural constraints or specialized loss functions that require prior knowledge of the noise distri…
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…
Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
Victor Sechaud, Laurent Jacques, Patrice Abry +1
Learning based methods are now ubiquitous for solving inverse problems, but their deployment in real-world applications is often hindered by the lack of ground truth references for…
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…