Adaptive-order statistic filters for noise characterization and suppression using noisy reference

In this paper several adaptive order statistic filters (OSF) are developed and compared for channel characterization and noise suppression in images and 3D CT data. Emphasis has been put on the situation when a noise-free reference image is not available but instead we can have a sequence of two noisy versions of the same image. One of the noisy images is used as the reference in the OSF. It is shown theoretically that if noises are not correlated, the expected values of the derived filter coefficients will be equal to those coefficients derived using a noise-free reference. Experiments using the noisy reference images yield comparable result to those methods using a noise-free reference image nd also better results than those of median, Gaussian, averaging and Wiener filters.