An algorithm for image noise-removal based on local adaptive
window size filtering is developed. Two features for use in local
spatial/transform-domain filtering are suggested. First, filtering is
performed on images corrupted not only by additive white noise, but also
by image-dependent (e.g. film-grain noise) or multiplicative noise.
Second, the used transforms are equipped with a varying adaptive window
size obtained by the intersection of confidence intervals (ICI) rule.
Finally, we combine all estimates available for each pixel from
neighboring overlapping windows by weighted averaging these estimates.
Comparison of the algorithm with the known techniques for noise removal
from images shows the advantage of the new algorithm, both
quantitatively and visually
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