Réunion

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.

Please note that the talks will be given in French.

Pour les membres du GdR IASIS qui souhaitent une prise en charge de leur mission par le GdR, anticipez votre demande : les demandes de missions doivent être formulées d'ici le 15 novembre.

Invited Speakers:

• Andrés Almansa (MAP5, Université de Paris)
• Pierre Chainais (Centrale Lille)
• Jean-Christophe Pesquet (Centrale Supélec)
• Nelly Pustelnik (CNRS, ENS Lyon)

Organizers:

• Julie Delon (MAP5, Université Paris Cité)
• Arthur Leclaire (LTCI, Télécom Paris)

This workshop is organized with the support of GDR IASIS and RT MAIAGES.