A Fuzzy Weighted Mean Aggregation Algorithm for Color Image Impulse Noise Removal

Jyh-Yeong Chang, Hui Ping Liu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


In this paper, we utilize fuzzy weighted mean aggregation algorithm to construct Interval-Valued Fuzzy Relations (IVFR) for grayscale image noise detection. To this end, we use two weighting parameters to calculate the weighted mean difference of the central pixel and its 8-neighborhood pixels in a sliding window across the image. Then, the central pixel will be identified as noisy or non-noisy by using a threshold operation. Besides, to decrease the noise pixel detection error, we have derived an iterative learning mechanism of these weighting parameters of the mean aggregation and thresholds in the training stage. Finally, we embed the pocket algorithm in our learning mechanism to train the best parameter set to minimize the noisy and noise free pixel detection error. The flexibility of the proposed IVFR approach is quite suited to learn the characteristics existing among the noisy pixel and its neighbors. Thus the derived IVPR scheme can excellently detect a noisy pixel and lead to marvelous result on impulsive noise removal.
Original languageEnglish
Title of host publicationIEEE International Conference on Automation Science and Engineering (CASE)
Number of pages6
StatePublished - 24 Aug 2015



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