Title :
A sure-fired way to choose smoothing parameters in ill-conditioned inverse problems
Author_Institution :
Dept. of Stat., Macquarie Univ., North Ryde, NSW, Australia
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;
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
DOI :
10.1109/ICIP.1996.560376
