Nonlinear order statistic filters for image filtering and edge detection |
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| Authors: | I Pitas A.N Venetsanopoulos |
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| Affiliation: | 1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, PR China;2. School of Computing and Engineering, University of West London, W5 5RF, UK;3. Yituyishu(Beijing) Technology Company Ltd., Beijing, PR China;4. Science and Technology on Communication Networks Laboratory, Shijiazhuang, PR China |
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| Abstract: | A novel class of nonlinear filters for image processing is proposed. This class is a combination of nonlinear mean and order statistic filters. Median, homomorphic, α-trimmed mean, nonlinear mean, order statistic, and linear filters can be considered as special cases of this class. The properties of these filters in the presence of different kinds of noise are investigated. It is shown that these filters can be used for the reduction of additive white noise, signal-dependent noise, and impulse noise. It is also shown that they preserve edges better than linear filters. Such filters can successfully be used as edge detectors, by appropriate adjustment of some of their parameters. Edge information can be used as an input to these filters to perform in an adaptive manner, changing their behaviour near the edges of an image. It is finally shown that many of the filters proposed have a reasonable (and in certain cases small) computational complexity. |
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| Keywords: | Edge detection nonlinear filters image processing image filtering order statistic filters |
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