1 code implementation • 25 May 2023 • Adrien Courtois, Damien Scieur, Jean-Michel Morel, Pablo Arias, Thomas Eboli
We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer.
no code implementations • 25 May 2023 • Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks.
no code implementations • 10 Mar 2023 • Jamy Lafenetre, Ngoc Long Nguyen, Gabriele Facciolo, Thomas Eboli
Image resolution is an important criterion for many applications based on satellite imagery.
no code implementations • 1 Aug 2022 • Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo
The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion.
no code implementations • 29 Jul 2022 • Bruno Lecouat, Thomas Eboli, Jean Ponce, Julien Mairal
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.
no code implementations • 13 Apr 2021 • Thomas Eboli, Jian Sun, Jean Ponce
We address the problem of non-blind deblurring and demosaicking of noisy raw images.
1 code implementation • ECCV 2020 • Thomas Eboli, Jian Sun, Jean Ponce
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
no code implementations • 16 Jun 2020 • Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis Bach, Jean Ponce, Alessandro Rudi
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.