Fast algorithms for training stack filters

Author

Lin, Jean-Hsang ; Kim, Yeong-Taeg

Author_Institution

Dept. of Electr. Eng., Delaware Univ., Newark, DE, USA

Volume

42

Issue

4

fYear

1994

fDate

4/1/1994 12:00:00 AM

Firstpage

772

Lastpage

781

Abstract

Stack filters constitute a class of nonlinear digital filters possessing a weak superposition property. In the paper, fast algorithms for training stack filters are introduced. The first algorithm is based on a technique previously developed. Similar to the previous algorithm, this improved method requires only simple arithmetic operations-increment, decrement, and local comparison. The ε-convergence property of the fast algorithm is established. In addition, two fast training algorithms are developed for a subclass of stack filters called weighted order statistic filters. These fast algorithms are variants of the LMS and RLS algorithms in linear filters. All three fast algorithms developed exploit the repetition property of the training input. The factor of speedup is approximately the ratio between the number of quantization levels of the input and the window size

Keywords

digital filters; filtering and prediction theory; image reconstruction; learning (artificial intelligence); ϵ-convergence property; decrement; factor of speedup; fast algorithms; increment; local comparison; nonlinear digital filters; number of quantization levels; repetition property; stack filters; training; weak superposition property; weighted order statistic filter; window size; Arithmetic; Digital filters; Filtering; Image edge detection; Image restoration; Least squares approximation; Nonlinear filters; Quantization; Resonance light scattering; Statistics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.285642

Filename

285642