Nonlinear Spectral Decompositions in Imaging and Inverse Problems

Explore nonlinear spectral decompositions in imaging and inverse problems through this virtual seminar talk by Martin Burger from FAU. Delve into a variational theory that extends classical spectral decompositions in linear filters and singular value decomposition of linear inverse problems to a nonlinear regularization setting in Banach spaces. Discover applications in imaging and data science, and learn about the computation of nonlinear eigenfunctions using gradient flows and power iterations. This one-hour talk, part of the IMAGINE OneWorld SIAM-IS Virtual Seminar Series, offers valuable insights for researchers and professionals in the fields of applied mathematics, imaging, and data science.