In minisymposium: Image Restoration and ReconstructionThu 16:00–16:30, Room AV 01.12
Tikhonov regularization is one of the most popular approaches to solving linear discrete ill-posed problems. The choice of regularization matrix may significantly affect the quality of the computed solution. When the regularization matrix is the identity, iterated Tikhonov regularization can yield computed approximate solutions of higher quality than (standard) Tikhonov regularization. We discuss convergence properties of iterated Tikhonov regularization with a regularization matrix different from the identity. Computed examples illustrate the competitiveness of this method.