In this presentation, we will present a few scientific imaging problems where hybrid approaches that combine physical models of image formation and deep learning are highly successful. We will then address a fundamental challenge in image restoration: the choice of estimator, as perceptual quality often does not align with traditional objective criteria such as minimizing the mean squared error. Finally, we will show how algorithms related to diffusion—highly successful in generative image modeling—can provide an effective solution to this problem.

These seminars are being made possible through the support of the CFM-ENS Chair « Modèles et Sciences des Données ».

The organizers: Giulio Biroli, Alex Cayco Gajic, Bruno Loureiro, Stéphane Mallat, Gabriel Peyré.