Generalized study of the weighted least-squares measure for the selection of the regularization parameter in inverse problems

Michael E. Zervakis, Taek M Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The minimization of the weighted least-squares (WLS) measure has been established as a good technique for the selection of the regularization parameter associated with the constrained least-squares restoration approach. In this paper we study the WLS measure when linear and nonlinear estimators are considered. Nonlinear estimators become increasingly important in association with robust restoration schemes. Such estimators often lack analytic interpretation rendering the direct computation of the WLS measure useless. It is shown that the value minimizing the WLS measure can be approximated by the solution of a nonlinear equation. The approximation improves as the signal-to-noise ratio increases. The efficiency of this approximation is verified through restoration examples in Laplacian noise.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherPubl by IEEE
Pages415-418
Number of pages4
Volume1
ISBN (Print)0780312813
StatePublished - Jan 1 1993
Event1993 IEEE International Symposium on Circuits and Systems - Chicago, IL, USA
Duration: May 3 1993May 6 1993

Other

Other1993 IEEE International Symposium on Circuits and Systems
CityChicago, IL, USA
Period5/3/935/6/93

Fingerprint Dive into the research topics of 'Generalized study of the weighted least-squares measure for the selection of the regularization parameter in inverse problems'. Together they form a unique fingerprint.

Cite this