Krylov subspace methods are powerful iterative regularization tools for large-scale linear inverse problems, such as those arising in image deblurring and computed tomography. We exploit a flexible version of some Krylov subspace methods, which uses adaptive preconditioning to promote TV-like regularization in the solution. Numerical experiments and comparisons with other well-known methods for the computation of large-scale solutions are presented.

This presentation is part of Minisymposium “MS8 - Krylov Methods in Imaging: Inverse Problems, Data Assimilation, and Uncertainty Quantification (2 parts)”
organized by: Arvind Saibaba (North Carolina State University) , Julianne Chung (Virginia Tech) , Eric de Sturler (Virginia Tech) .

Authors:

Malena Sabate Landman (University of Bath)

Silvia Gazzola (University of Bath)

Keywords: