Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. Unfortunately, linear systems arising in image processing are highly ill-conditioned and preconditioners often give bad results, since the noise components on the data are strongly amplified already at the early iterations. In this work, we propose filtering strategies which allow to obtain preconditioners with rgularization features for Toeplitz systems of image deblurring. Regularization preconditioners are able to speed up the convergence in the space less sensitive to the noise and, simultaneously, they slow down the restoration from components mainly corrupted by noise. A 2-d numerical simulation concerning astronomical image deblurring confirms the effectiveness of the arguments.
Serra-Capizzano recently introduced anti-reflecting boundary conditions (AR-BC) for blurring models: the idea seems promising both from the computational and approximation viewpoint. The key point is that, under certain symmetry conditions, the AR-BC matrices can be essentially simultaneously diagonalized by the (fast) sine transform DST I and, moreover, a C1 continuity at the border is guaranteed in the 1D case. Here we give more details for the 2D case and we perform extensive numerical simulations which illustrate that the AR-BC can be superior to Dirichlet, periodic and reflective BCs in certain applications.