Many interesting image processing problems can be solved by finding suitable minimizers of a variational model. In this talk, we consider models where the unknowns reside in a space of measures. We will consider theoretical results and discuss some applications, in particular in diffusion-weighted imaging, manifold-valued optimization and approximation of non-convex problems.
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