Deep learning has revolutionised image processing and is often considered to outperform classical approaches based on accurate modelling of the image formation process. In this presentation, we will discuss the interplay between model-based and learning-based paradigms, and show that hybrid approaches show great promises for scientific imaging, where interpretation and robustness to real-world degradation is important. We will present two applications on super-resolution and high-dynamic range imaging, and exoplanet detection from direct imaging at high contrast.

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