Inverse Problems, Krylov Methods, and Dog Pictures

Abstract

Solving inverse problems in imaging can be very challenging due to the inherent ill-posedness and high dimensionality. To deal with this, one often employs efficient regularisation methods that enforce some additional known properties of the solution. In this talk, we shall consider Krylov subspace-based regularization approaches that combine direct matrix factorization methods on small subproblems with iterative solvers.