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Local transform-based image denoising with adaptive window-size selection

An algorithm for image noise-removal based on local adaptive filtering is proposed in this paper. Three features to use into the local transform-domain filtering are suggested. First, filtering is performed on images corrupted not only by an additive white noise, but also by image-dependent (e.g. film-grain noise) or multiplicative noises. Second, a number of transforms is used instead of the single one, the resulting estimate is a linear combination of estimates from each of the transforms using local statistics. Third, these transforms are equipped with a varying adaptive window size for which we use the so-called intersection of confidence intervals (ICI) rule. Finally, we combine all the estimates for a pixel from neighboring windows by weighted averaging them. Comparison of the algorithm with known techniques for noise removal from images shows the advantage of the new approach, both quantitatively and visually.