REGULARIZATION PARAMETER SELECTION METHODS FOR ILL POSED POISSON IMAGING PROBLEMS

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Grantor dc:publisher

University of Montana

Year

2010

Author dc:creator

• Goldes, John

Repository record dc:identifier

https://scholarworks.umt.edu/etd/811

OAI identifier oai:identifier

oai:scholarworks.umt.edu:etd-1830