TY - CHAP
T1 - A Fuzzy Weighted Mean Aggregation Algorithm for Color Image Impulse Noise Removal
AU - Chang, Jyh-Yeong
AU - Liu, Hui Ping
PY - 2015/8/24
Y1 - 2015/8/24
N2 - 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.
AB - 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.
KW - REDUCTION
U2 - 10.1109/CoASE.2015.7294273
DO - 10.1109/CoASE.2015.7294273
M3 - Chapter
SP - 1268
EP - 1273
BT - IEEE International Conference on Automation Science and Engineering (CASE)
PB - IEEE
ER -