The University of Arizona

MCMC for High Energy X-Ray Radiography

MCMC for High Energy X-Ray Radiography

Series: Program in Applied Mathematics Brown Bag Seminar
Location: Math 402
Presenter: Jesse Adams, Program in Applied Mathematics, University of Arizona

Image deblurring via deconvolution can be formulated as a hierarchical Bayesian inverse problem, and numerically solved by Markov Chain Monte Carlo (MCMC) methods. Numerical solution is difficult because

  • inconsistent assumptions about the data outside of the field of view of the image lead to artifacts near the boundary; and
  • the Bayesian inverse problem is high-dimensional for high-resolution images.

The numerical MCMC framework I present addresses these issues. Boundary artifacts are reduced by reconstructing the image outside the field of view. Numerical difficulties that arise from high-dimensions are mitigated by exploiting sparse problem structure in the prior precision matrix.

Department of Mathematics, The University of Arizona 617 N. Santa Rita Ave. P.O. Box 210089 Tucson, AZ 85721-0089 USA Voice: (520) 621-6892 Fax: (520) 621-8322 Contact Us © Copyright 2018 Arizona Board of Regents All rights reserved