A sure-fired way to choose smoothing parameters in ill-conditioned inverse problems

Author

Solo, Victor

Author_Institution

Dept. of Stat., Macquarie Univ., North Ryde, NSW, Australia

Volume

3

fYear

1996

fDate

16-19 Sep 1996

Firstpage

89

Abstract

Regularisation methods for the solution of inverse problems are well known although the theoretical study of their performance especially in image processing contexts is not well advanced. What is also much less resolved is smoothing or penalty parameter estimation. We describe a general procedure for estimation of auxiliary finite dimensional parameters in ill-conditioned inverse problems. The method is applicable to nonlinear problems, involves no approximations but offers computational advantages over cross validation and maximum likelihood

Keywords

image processing; inverse problems; parameter estimation; smoothing methods; auxiliary finite dimensional parameters; cross validation; ill-conditioned inverse problems; image estimation; image processing; maximum entropy; maximum likelihood estimation; neural networks; nonlinear problems; penalty parameter estimation; positive signal estimation; regularisation methods; smoothing parameters; Astronomy; Biomedical engineering; Biomedical imaging; Inverse problems; Mathematics; Maximum likelihood estimation; Parameter estimation; Smoothing methods; Statistics; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560376

Filename

560376