Hybrid Approach of Efficient Decision-Based Algorithm and Fuzzy Logic for the Removal of High Density Salt and Pepper Noise in Images

Abstract

In this paper, this hybrid approach of efficient decision-based scheme and fuzzy logic are proposed for the restoration of gray scale and color images that are heavily corrupted by salt and pepper noise. The processed pixel is examined for 0 or 255; if found true, then it is considered as noisy pixel else not noisy. If found noisy the four neighbors of the noisy pixels are checked for 0 or 255. If all the four neighbors of the corrupted pixel are noisy, the mean of the four neighbors replaces the corrupted pixel. If any of the four neighbors is a non-noisy pixel, the number of corrupted pixels is calculated in the current processing window. If the count is less than three, then the noisy pixel is replaced by an unsymmetrical trimmed median. If the current window has more than three noisy pixels, then unsymmetrical trimmed mean replaces the corrupted pixels. If all the pixels of the current processing window are noisy then instead of enhanced decision-based algorithm, the fuzzy membership function of the window is replaced as output processing pixel. The uncorrupted pixel is left unchanged. The proposed algorithm is tested on various gray scale and color images and found that it gives excellent PSNR, high IEF and lowest MSE. Also it preserves the image features like the edges and color components at higher noise densities. The quality of the results of proposed algorithm is superior when compared to the various existing state-of-the-art methods.