Mat-1.3656 Seminar on numerical analysis and computational science

Monday, Nov 18, 2013, room M233 at 14.15, Eirola & Stenberg


Lauri Harhanen,  Aalto!
Edge-preserving priorconditioning for linear ill-posed problems:

We present a method for solving large-scale linear inverse problems regularized with a nonlinear, edge-preserving penalty term such as the total variation or Perona–Malik. In the proposed scheme, the nonlinearity in the regularizer is handled with lagged diffusivity fixed point iteration which involves solving a large-scale linear least squares problem in each iteration. Because of the notoriously slow convergence of Krylov methods for problems of this type, we propose to accelerate it by means of priorconditioning. Priorconditioning is a technique which embeds the information contained in the prior or regularizer directly into the forward operator through preconditioning and leads to tremendously accelerated convergence.