This paper deals with the application of non-convex, sublinear penalty terms
to the regularization of possibly non-linear inverse problems
the solutions of which are assumed to have a sparse expansion with respect
to some given basis or frame.
It is shown that this type of regularization
is well-posed and yields sparse results.
Moreover, linear convergence rates are derived under the additional
assumption of a certain range condition.