Webinaires

Physical Models and Machine Learning for Scientific Imaging

Annonce

Résumé

Deep learning has transformed image processing, often surpassing traditional methods that rely on precise modeling of optical image formation. In this presentation, we explore the synergy between model-based and learning-based approaches, highlighting the potential of hybrid methods for scientific imaging—an area where interpretability, robustness to real-world degradations, and the scarcity of ground truth data pose major challenges. We will showcase several applications, from high-contrast direct imaging for exoplanet detection to molecular-scale microscopy, illustrating how these hybrid strategies can push the boundaries of what is achievable in complex imaging scenarios.