Removal of high density salt and pepper noise through improved tolerance based selective arithmetic mean filtering with wavelet thresholding

In this paper, an effective improved tolerance based selective arithmetic mean filtering technique (ITSAMFT) combined with wavelet thresholding is proposed for restoration of images that are highly corrupted by salt and pepper noise. The proposed method first detects the noisy pixel based on the difference between the pixel of interest and arithmetic mean (AM). After detection, noisy pixels are filtered out by using the improved tolerance based adaptive masking selective arithmetic mean filtering and wavelet thresholding procedure. Computer simulation is carried out on the benchmark images and the results are compared with the state of the art studies to demonstrate the performance of the proposed technique. The results show that the ITSAMFT combined with wavelet thresholding provides better performance than already reported filters in terms of high peak signal to noise ratio (PSNR), good structural similarity (SSIM), correlation (CORR) and good visual quality even at noise density as high as 90%.