Efficient decision based algorithm for the removal of high density salt and pepper noise in images

In this paper, an efficient decision based scheme is proposed for the restoration of grayscale and colour 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 neighbours of the noisy pixels are checked for 0 or 255. If all the four neighbours of the corrupted pixel are noisy, the mean of the four neighbours replaces the corrupted pixel. If any of the four neighbours is a non-noisy pixel, calculate the number of corrupted pixels 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 unsymmetrical trimmed mean, global mean of the image is replaced as output. The uncorrupted pixel is left unchanged. The proposed algorithm is tested on various grayscale and colour images and found that it gives excellent PSNR, high IEF and lowest MSE. Also it consumes average time with excellent edge preservation even at higher noise densities. The quality of the results of proposed algorithm is superior when compared to the various state of the art methods.