Decision-based median filter improved by predictions

This paper presents a decision-based median filtering algorithm in which local image structures are used to estimate the original values of the noisy pixels. The decision whether a pixel is corrupted or not is based on a new decision measure which considers the differences of adjacent pixel values in the rank-ordered sequence. Once the pixels in a noisy image have been classified into uncorrupted and noise-corrupted ones, the blocks containing only the uncorrupted pixels are used to train the predictive relationship between the center pixel and its neighbors, which is represented by a function approximation f. By applying f to noise-corrupted blocks, we could generate the candidates of the original value of a noise-corrupted pixel, and estimate it using median filtering of the candidates.