Nonlinear Spectral Decompositions in Imaging and Inverse Problems

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.