Otmar Scherzer

Computational Science Center, University of Vienna

Regularization of Inverse Problem MT2

Inverse Problems is an interdisciplinary research area with profound applications in many areas of science, engineering, technology, and medicine. Nowadays, a core technique for solving imaging problems are regularization methods. The foundations of these approximation methods were laid by Tikhonov decades ago, when he generalized the classical definition of well-posedness. In the early days of regularization methods, they were analyzed mostly theoretically, while later on numerics, efficient solutions, and applications of regularization methods became important. This Minitutorial gives a survey on theoretical developments in regularization theory: Starting from quadratic regularization methods for linear ill-posed problems, to convex regularization, and to non-convex regularization methods of non-linear problems. The theoretical analysis will be supported by particular imaging examples.

Chair: Per Christian Hansen (Technical University of Denmark)

The slides are available here

  Thu 07 June at 09:30 Room B (Palazzina A - Building A, floor 1)