Using Generalized Fuzzy Weighted Mean Aggregation in Impulse Noise Removal of Images

• Main Authors: Chen, Wei-Han, 陳韋翰
• Other Authors: Chang, Jyh-Yeong
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/67273500132487858428

碩士 === 國立交通大學 === 電控工程研究所 === 103 === In this thesis, we have porposed weighted mean aggregation to construct interval-valued fuzzy relation for grayscale and color images noise detection. In the beginning, we employ two weighting parameters, and perform the weighted mean aggregation for the central pixel and its eight neighbor pixels in a sliding window across the image to lead to the fuzzy images. In the end, the image noise map is obtained through a suitable thresholding. Moreover, to decrease the noisy and un-contaminated pixel detection error, we have derived the 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 testing stage is composed of three component: image histogram, noise detection, and image restoration. First, we calculate the histogram of the testing image to find the groups of potential noise pixels. On these possible noisy pixel groups, we make use of the best weighting parameters trained to perform the fuzzy weighted mean aggregation to double-check whether they are noise corrupted or not. Furthermore, we utilize the Weighted Average Score (WAS) method to integrate information in the training stage to enhance the accuracy of our noise detector in noise detection step. In other words, we not only use the parameters obtained by the training stage, but also use the information which is obtained during training stage. Finally, if a pixel is identified as noisy in previous step, its value will be replaced by a weighted mean filter. According to the simulation results, we have found that our proposed algorithm provide a significant improvement over other existing filers.