Modern Regularisation


The availability of expressive regularizers is a very important component in solving ill-posed inverse problems in imaging. In recent years, hand-designed regularizers have been gradually replaced by data-driven ones. Provided sufficient training data is available, it is nowadays possible to learn tailored regularizers for a certain problem class. This usually leads to a huge increase in reconstruction quality, but the learned regularizers are usually much harder to analyse and it is much harder to give guarantees on convergence behaviour, generalisation ability, or reconstruction error. In this session, we will present and discuss the latest methods, techniques and applications in this cutting-edge field of research.

Organisers: Thomas Pock (Chair) & Philip Schniter (Committee member)

Session Schedule

17:30 - 17:50 Invited talk
Thomas Pock
17:50 - 18:10 Invited talk
Stancey Levine
18:10 - 18:30 Invited talk
Yaniv Romano
18:30 - 18:50 Invited talk
Martin Holler
18:50 - 19:10 Invited talk
Jong Chul Ye
19:10 - 20:30 Invited poster
Markus Haltmeier
19:10 - 20:30 Committee member poster
Philip Schniter
19:10 - 20:30 Contributed poster
Alin Achim
19:10 - 20:30 Contributed poster
Amir Aghabiglou
19:10 - 20:30 Contributed poster
Danica B. Fliss