  Convergence rates results for variational regularization methods typically
assume the regularization functional to be convex. While this assumption is
natural for scalar-valued functions, it can be unnecessarily strong for
vector-valued ones. In this paper we focus on regularization functionals with
polyconvex integrands. Even though such functionals are nonconvex in general,
it is possible to derive linear convergence rates with respect to a generalized
Bregman distance, an idea introduced by Grasmair in 2010. As a case example we
consider the image registration problem.
