A new evaluation method for image noise reduction and usefulness of the spatially adaptive wavelet thresholding...

We have proposed a direct evaluation method concerning preservation of noise-free components for image noise reduction. This evaluation method is to graphically estimate how well a noise-reduction method will preserve noise-free image components by using the normal probability plot of the image pixel value difference between an original image and its noise-reduced image; this difference is equivalent to the "method noise" which was defined by Buades et al. Further, by comparing the linearity of a normal probability plot for two different noise reduction methods, one can graphically assess which method will be more able to preserve the noise-free component than the other. As an illustrative example of this evaluation method, we have evaluated the effectiveness of the spatially-adaptive BayesShrink noise-reduced method devised by Chang et al., when applied to chest phantom CT images. The evaluation results of our proposed method were consistent with the visual impressions for the CT images processed in this study. The results of this study also indicate that the spatially-adaptive BayesShrink algorithm devised by Chang et al. will work well on the chest phantom CT images, although the assumption for this method is often violated in CT images, and the assumption postulated for the spatially-adaptive BayesShrink method is expected to have sufficient robustness for CT images.