Filtering noise in color images using adaptive-neighborhood statistics

Various nonlinear, fixed-neighborhood techniques based on local statistics have been proposed in the literature for filtering noise in color images. We present adaptive-neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel (called the “seed” when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double-window modified trimmed-mean filter, the generalized vector directional filter – double-window – α-trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive nonparametric filter with Gaussian kernel. It is shown that the ANF techniques provide better visual results, effectively suppressing noise while not blurring edges; the results are also better in terms of objective measures (such as normalized mean-squared error and normalized color difference) than the results of the other methods.