Year of Award
Doctor of Philosophy (PhD)
Other Degree Name/Area of Focus
Department or School/College
Department of Mathematical Sciences
John M. Bardsley
Leonid Kalachev, Emily Stone, Jennifer Halfpap, Jesse Johnson
University of Montana
A common problem in imaging science is to estimate some underlying true image given noisy measurements of image intensity. When image intensity is measured by the counting of incident photons emitted by the object of interest, the data-noise is accurately modeled by a Poisson distribution, which motivates the use of Poisson maximum likelihood estimation. When the underlying model equation is ill-posed, regularization must be employed. I will present a computational framework for solving such problems, including statistically motivated methods for choosing the regularization parameter. Numerical examples will be included.
Goldes, John, "REGULARIZATION PARAMETER SELECTION METHODS FOR ILL POSED POISSON IMAGING PROBLEMS" (2010). Graduate Student Theses, Dissertations, & Professional Papers. 811.
© Copyright 2010 John Goldes