Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution. Krylov subspace methods are often ideally suited for this task: their iterative nature is a natural way to handle such large-scale problems, and the underlying Krylov subspace provides a convenient mechanism to regularized the problem by projecting it onto a low-dimensional "signal subspace" adapted to the particular problem. In this talk we consider the three Krylov subspace methods CGLS, MINRES, and GMRES. We describe their regularizing properties, and we discuss some computational aspects such as preconditioning and stopping criteria.
|Title of host publication||Proceedings of WSC 2011|
|Publication status||Published - 2011|
|Event||36th Conference of the Dutch-Flemish Numerical Analysis Communities: Woudschouten - Zeist, Netherlands|
Duration: 5 Oct 2011 → 7 Oct 2011
Conference number: 36
|Conference||36th Conference of the Dutch-Flemish Numerical Analysis Communities|
|Period||05/10/2011 → 07/10/2011|