Two-dimensional joint process adaptive filtering via principal component support region

Author

Kim, Dai I. ; De Wilde, P.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

fYear

1999

fDate

36373

Firstpage

545

Lastpage

553

Abstract

This paper describes a 2-D joint process adaptive filtering algorithm using the orthogonal property of the principal component support region in order to speed up the convergence rate. We also introduce the symmetrical sparse support region (SSSR) to reduce the computational burden of the duplicated support region (DSR). Computer simulation results are given to verify the performance of the proposed model

Keywords

adaptive filters; computational complexity; convergence of numerical methods; image restoration; least mean squares methods; principal component analysis; two-dimensional digital filters; DSR; SSSR; computational burden; convergence rate; duplicated support region; orthogonal property; performance; principal component support region; symmetrical sparse support region; two-dimensional joint process adaptive filtering; Adaptive filters; Biomedical imaging; Computer simulation; Convergence; Educational institutions; Electronic mail; Filtering algorithms; Image restoration; Least squares approximation; Signal generators;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Conference_Location

Madison, WI

Print_ISBN

0-7803-5673-X

Type

conf

DOI

10.1109/NNSP.1999.788174

Filename

788174