“Regularization Parameter Selection Methods for Ill-Posed Poisson Imaging Problems”
Document Type
Presentation Abstract
Presentation Date
5-7-2010
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.
Recommended Citation
Goldes, John, "“Regularization Parameter Selection Methods for Ill-Posed Poisson Imaging Problems”" (2010). Colloquia of the Department of Mathematical Sciences. 352.
https://scholarworks.umt.edu/mathcolloquia/352
Additional Details
Doctoral Dissertation Defense. Link to the presenter's dissertation.
Friday, May 7, 2010
3:10 pm in Math 305