ABSTRACT
In this paper we study well-posedness of a class of nonconvex variational principles arising in regularization theory for denoising of data with sampling errors and level set regularization methods for inverse problems. These models result in minimization of nonconvex, singular functionals involving (possibly) non-local operators.
ACKNOWLEDGMENT
The work of the authors is supported by FWF (Austrian Science Fund) Project Y-123INF and P-15617.