Title :
Adaptive SFWO filter design
Author :
Öten, Remzi ; de Figueiredo, Rui J.P.
Author_Institution :
Lab. for Machine Intelligence, California Univ., Irvine, CA, USA
Abstract :
In this paper we introduce a new design strategy for SFWO (sampled function weighted order) filters based on approximation theory. It is shown that with a good choice of noise tail-length estimator and a good approximation of the relation between this estimate and filter parameters, the adaptive SFWO filter gives very promising results when the noise type is unknown or varies with time. For this use, we also introduced a new tail-length estimator based on robust statistics
Keywords :
adaptive filters; adaptive signal processing; approximation theory; digital filters; filtering theory; image restoration; network synthesis; noise; signal sampling; statistical analysis; adaptive SFWO filter design; approximation theory; filter parameters; image restoration; noise tail-length estimator; robust statistics; sampled function weighted order filters; Adaptive filters; Computer vision; Filtering theory; Laboratories; Laplace equations; Machine intelligence; Maximum likelihood estimation; Noise robustness; Parameter estimation; Statistics;
Conference_Titel :
Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-8186-8821-1
DOI :
10.1109/ICIP.1998.723719
