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

Author

Zervakis, Michael E. ; Kwon, Taek Mu

Author_Institution

Dept. of Comput. Eng., Minnesota Univ., Duluth, MN, USA

fYear

1993

fDate

3-6 May 1993

Firstpage

415

Abstract

The weighted least-squares (WLS) measure is studied 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 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

Keywords

image restoration; inverse problems; least squares approximations; random noise; Laplacian noise; WLS measure; nonlinear estimators; regularization parameter in inverse problems; robust restoration schemes; signal-to-noise ratio; weighted least-squares measure; Biomedical measurements; Degradation; Eigenvalues and eigenfunctions; Image processing; Image restoration; Inverse problems; Nonlinear equations; Pollution measurement; Signal restoration; Weight measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.393746

Filename

393746