Surrogate Functional Based Subspace Correction Methods for Image Processing

In this paper we present subspace correction methods for non-smooth and non-additive energies which are guaranteed to converge. Moreover, we are able to provide an estimate of the distance between the outcome of the subspace correction method and the global minimizer of the non-smooth and non-additive objective. With the help of this estimate we can finally show in our numerical experiments that the proposed method even converges to a true global minimizer.