A general formulation of the weighted smoothing functional for regularized image restoration

Author

Kang, Moon Gi ; Katsaggelos, Aggelos K.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA

Volume

2

fYear

1994

fDate

13-16 Nov 1994

Firstpage

695

Abstract

Proposes a general form of the weighted smoothing functional for regularized image restoration. The weighting matrices which introduce the spatial adaptivity are defined as a function of the (partially) restored image. As a result no prior knowledge about the image is required but the smoothing functional to be minimized is nonlinear with respect to the unknown image. Conditions for the convexity of the functional are established. An iterative algorithm is proposed for obtaining its minimum. Sufficient conditions for the convergence of the algorithm are established. Various forms of the weighting matrices are proposed. Experimental results demonstrate the effectiveness of the approach

Keywords

adaptive signal processing; convergence of numerical methods; image restoration; iterative methods; matrix algebra; minimisation; smoothing methods; convergence; convexity; iterative algorithm; nonlinear smoothing functional; partially restored image; regularized image restoration; spatial adaptivity; weighted smoothing functional; weighting matrices; Additive noise; Bayesian methods; Classification algorithms; Convergence; Image restoration; Iterative algorithms; Moon; Smoothing methods; Stochastic processes; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference

Conference_Location

Austin, TX

Print_ISBN

0-8186-6952-7

Type

conf

DOI

10.1109/ICIP.1994.413660

Filename

413660