Decision-based median filter using k-nearest noise-free pixels

Yi Hong, Sam Kwong, Hanli Wang

Abstract: Traditional median filter replaces each pixel in an image with the median value of their k-nearest pixels (commonly known as pixels in 2-D window). The problem associated with this approach is that the restored pixel is noise if median value of their k-nearest pixels is a corrupted pixel. To mitigate the above problem, this paper proposes a novel decision-based median filter that replaces each corrupted pixel with the median value of their k-nearest noise-free pixels. Advantages of the median filter using k-nearest noise-free pixels instead of k-nearest pixels are two facets: first, it guarantees that pixels after being restored must be noise-free, because the median filter operator is executed on noise-free pixels; second, the median filter using k-nearest noise-free pixels adaptively adjusts its window size for each pixel such that the number of noise-free pixels locating in the window increases up to k. To realize it, the median filter using k-nearest noise-free pixels firstly detects noise-free pixels in an image, then replaces each corrupted pixel with the median value of their knearest noise-free pixels. The proposed median filter is tested on four real images corrupted by different levels of salt-and-pepper noise. Experimental results confirm the effectiveness of decision-based median filter using k-nearest noise-free pixels.