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Proceedings Paper

Comparison Study Of Non-Linear Filters In Image And Signal Processing Applications
Author(s): Yu- Shan Fong; Carlos A. Pomalaza-Raez; Xiao-Hua Wang
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Paper Abstract

This study presents a summary of application of non-linear filters in signal processing, based on robust estimation theory. Non-linear filters are used in many applications including speech and image processing, mainly because of their ability to suppress noise and preserve signal features such as edges. This is a property that linear filters do not have. In the past several years, many types of non-linear filters have been propsed. However, each of these filters shows optimal smoothing efficiency only for a specific type of noise or a specific type of image. In order to further apply these filters in speech and image processing properly, it is important to conduct a review and analysis of all these filters and give an objective evaluation of their performance. Based upon the point estimation theory, three classes of estimations can be distinguished, namely L-estimators, which are based on linear combination of order statistics; R-estimators, which are derived from rank tests; and M-estimators - maximum likelyhood estimators. The first part of the work carried out in this study is classification of various approaches of non-linear filters in these three types of estimators according to the behavior of the filter itself. The second part is the computer implementation of all the filters discussed. Finally the summary of experimental results are presented.

Paper Details

Date Published: 25 October 1988
PDF: 16 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.968936
Show Author Affiliations
Yu- Shan Fong, Clarkson University (United States)
Carlos A. Pomalaza-Raez, Clarkson University (United States)
Xiao-Hua Wang, Clarkson University (United States)

Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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