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. In the pixel restoration stage, we propose a new filtering method. It is divided into three steps: 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 trained weighting parameters to do the fuzzy weighted mean aggregation to double-check whether they are noise corrupted or not. If a pixel is identified as noisy, its value will be restored by a weighted mean filter. Simulation results show that the proposed algorithm provides a significant improvement over other existing filters and preserves more image details. Our algorithm can barely restore the image even when the noise rate is as high as 97 %.
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