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Proceedings Paper

Adaptive weighted median filter utilizing impulsive noise detection
Author(s): Jun Ishihara; Mitsuhiko Meguro; Nozomu Hamada
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Paper Abstract

The removal of noise in image is one of the current important issues. It is also useful as a preprocessing for edge detection, motion estimation and so on. In this paper, an adaptive weighted median filter utilizing impulsive noise detection is proposed for the removal of impulsive noise in digital images. The aim of our proposed method is to eliminate impulsive noise effectively preserving original fine detail in images. This aim is same for another median-type nonlinear filters try to realized. In our method, we use weighted median filter whose weights should be determined by balancing between the signal preserving ability and noise reduction performance. The trade off between these two inconsistent properties is realized using the noise detection mechanism and optimized adaptation process. In the previous work, threshold value between the signal and the output of the median filter have to be decided for the noise detection. Adaptive algorithm for optimizing WM filters uses the teacher image for training process. In our method, following two new approaches are introduced in the filtering. (1) The noise detection process uses the discriminant method to the histogram distribution of the derivation from median filter output. (2) Filter weights which have been learned by uncorrupted pixels and their neighborhood without the original image are used for the restoration filtering for noise corrupted pixels. The validity of the proposed method is shown through some experimental results.

Paper Details

Date Published: 18 October 1999
PDF: 9 pages
Proc. SPIE 3808, Applications of Digital Image Processing XXII, (18 October 1999); doi: 10.1117/12.365852
Show Author Affiliations
Jun Ishihara, Keio Univ. (Japan)
Mitsuhiko Meguro, Keio Univ. (Japan)
Nozomu Hamada, Keio Univ. (Japan)

Published in SPIE Proceedings Vol. 3808:
Applications of Digital Image Processing XXII
Andrew G. Tescher, Editor(s)

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