Title :
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
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;
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
DOI :
10.1109/NNSP.1999.788174
