Computational Imaging and Learning

CIL

In our unit, we combine physical and mathematical modelling with optimisation and learning techniques to develop robust image reconstruction methods that perform effectively even under limited training data and potentially complex physical models. We focus particularly on modelling spatial distortions, non-Gaussian noise statistics, and the stochastic, time-dependent dynamics that arise in various fluorescence microscopy modalities. Our ultimate objective is to integrate these models into innovative reconstruction methods that fuse physics, optimization, and (deep) learning approaches.