Krylov, Bayes and L2 magic.MS8

The success of the compressed sensing algorithms is based on the observation that if the underlying image is known to be sparse, or nearly black, a good approximation can be found as the minimizer of the ell-1 norm. Bayesian hypermodels using conditionally Gaussian priors provide a competitive alternative, in particular when combined with Krylov subspace iterative solvers. We address both computational and theoretical aspects of the proposed iterative algorithms.

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) .

Daniela Calvetti (Case Western Reserve University)
Erkki Somersalo (Case Western Reserve University)
Alexander Strang (Case Western Reserve University)
bayesian methods, image reconstruction, inverse problems, numerical linear algebra, statistical inverse estimation methods