Coupled regularization with multiple data discrepancies MS23

We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. This is motivated by applications where one aims to exploit correlations of different data channels for the ill-posed inversion. We consider a rather general setting and derive stability and convergence results. In particular, we show how parameter choice strategies adapted to the interplay of different discrepancies improve convergence rates.

This presentation is part of Minisymposium “MS23 - Multi-Modality/Multi-Spectral Imaging and Structural Priors (2 parts)
organized by: Matthias J. Ehrhardt (University of Cambridge) , Simon Arridge (University College London) .

Martin Holler (École Polytechnique, Université Paris Saclay)
Richard Huber (University of Graz)
Florian Knoll (New York University)