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Found 4 papers, 2 papers with code
Bilevel Imaging Learning Problems as Mathematical Programs with Complementarity Constraints: Reformulation and Theory
We investigate a family of bilevel imaging learning problems where the lower-level instance corresponds to a convex variational model involving first- and second-order nonsmooth sparsity-based regularizers.
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".
The structure of optimal parameters for image restoration problems
The analysis is done on the original -- in image restoration typically non-smooth variational problem -- as well as on a smoothed approximation set in Hilbert space which is the one considered in numerical computations.
Bilevel approaches for learning of variational imaging models
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.
