  Inverse problems are characterized by their inherent non-uniqueness and
sensitivity with respect to data perturbations. Their stable solution requires
the application of regularization methods including variational and iterative
regularization methods. Superiorization is a heuristic approach that can steer
basic iterative algorithms to have small value of certain regularization
functional while keeping the algorithms simplicity and computational efforts,
but is able to account for additional prior information. In this note, we
combine the superiorization methodology with iterative regularization methods
and show that the superiorized version of the scheme yields again a
regularization method, however accounting for different prior information.
