Nous vous rappelons que, afin de garantir l'accès de tous les inscrits aux salles de réunion, l'inscription aux réunions est gratuite mais obligatoire.
12 personnes membres du GdR ISIS, et 9 personnes non membres du GdR, sont inscrits à cette réunion.
Capacité de la salle : 150 personnes.
Many algorithms for image restoration and editing are based on some kind of regularity prior that is directly learned from data. This encompasses patch-based methods, plug-and-play methods, and more recently diffusion models. These methods are based on iterative algorithms whose convergence study raises difficult questions. For example, using a neural network to encode the image prior often requires to choose or design specific network architectures in order to keep convergence guarantees.
The goal of this day is to give an overview of the current knowledge on these learning-based image restoration algorithms. The emphasis will be put on methods that use a regularization encoded through a deep neural network, should it be on theoretical aspects (convergence, a posterior sampling, characterization of cluster points) or applications (for example in medical or satellite imaging). We will encourage contributions that aim to measure the reliability or stability of the methods, to quantize the uncertainty of the restoration, or to analyze the reconstruction artifacts created by these methods.
If you wish to propose a talk, please answer the following call for contributions before November 4th, 2024:
https://docs.google.com/forms/d/e/1FAIpQLSeJf8EsYlqBAev-ktKgsLiJl2i9PK96FNnRPBxutIkUav100Q/viewform
This workshop is organized with the support of GDR IASIS and RT MAIAGES.
Date : 2024-12-09
Lieu : Paris, Institut Henri Poincaré
Thèmes scientifiques :
(c) GdR IASIS - CNRS - 2024.