EVENTS

This 7-hours course covers typical mathematical tasks and caveats of image reconstruction problems. We introduce the theoretical framework of inverse problems and how different imaging modalities and measurement errors can lead to unfavorable reconstructions. Furthermore, we consider regularization strategies to overcome these issues, both from the theoretical and practical side. This fundamental knowledge is to be used as a starting point for self-guided learning during and beyond the course time. Basing off of the classical regularization approaches, the course further gives an introduction into deep learning for inverse problems. Finally, we also explore the Bayesian viewpoint, where we also consider the problem of uncertainty quantification.

The workshop covers a lecture and a tutorial part with hands-on, during which the instructors are available for instructions, feedback and advice.

NOTE: Registration will open August 19, 2026, 12 pm.