Compressed sensing and sparse recovery of signals and images has become a popular topic recently. ln this talk, we provide a dual viewpoint on sparse recovery and discuss (adaptive) inverse scale space methods for the efficient computational solutions. Surprisingly, the latter connect various different computational approaches for sparse recovery such as variational, iterative, and greedy methods, and they can be generalized from l1-minimization to a wider range of variational problems. This is also relevant for some applications we discuss, ranging from image reconstruction to the unmixing of spectral data.

Referent: Prof. Dr. Martin Burger, Westfälische Wilhelms-Universität Münster