Decision Based Detail Preserving Algorithm for the Removal of Equal and Unequal Probability Salt and Pepper Noise in Images and Videos

A novel vicinity based algorithm for the elimination of equal and unequal probability salt and pepper noise with a fixed 3x3 kernel is proposed. The proposed method uses a tree based switching mechanism for the replacement of corrupted pixel. The processed pixel is checked for 0 or 255; if found true then the pixel is considered as noisy else termed non noisy and left unaltered. If the pixel is noisy then it checks for the 4 neighbors of the processed pixel. If all the 4 neighbors are noisy then mean of the 4 neighbors are replaced. If any of the 4 neighbors are not noisy then the corrupted pixel is replaced by unsymmetrical trimmed mean. Under high noisy conditions if all the elements of the current processing window is noisy then global mean replaces the corrupted pixel. The proposed algorithm exhibits better performance both quantitatively and qualitatively over the standard and existing algorithms at very high noise densities. The performance of the existing non linear filters are outclassed by the proposed algorithm in terms of PSNR, IEF, MSE, and SSIM and also preserves fine details of an image even at high noise densities. The algorithm works well even for gray scale, color images and video.