A Complete Computational Framework for Ill-Posed Poisson Maximum Likelihood Estimation

Authors

John Goldes, The University of Montana

Document Type

Presentation Abstract

Presentation Date

3-1-2010

Abstract

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.

Additional Details

Monday, 1 March 2010
3:10 p.m. in Math 103
4:00 p.m. Refreshments in Math Lounge 109

Recommended Citation

Goldes, John, "A Complete Computational Framework for Ill-Posed Poisson Maximum Likelihood Estimation" (2010). Colloquia of the Department of Mathematical Sciences. 339.
https://scholarworks.umt.edu/mathcolloquia/339