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Data-dependent median filtering with multi-windows for restoration of images corrupted by high probability impulsive noise

The authors propose a new method for restoring images corrupted by high-probability impulsive noise at 10 to 50%. The strength (number of instances) of impulsive noise present in the area adjacent to a processing point is not uniform even in images that are corrupted by high-probability impulsive noise. In addition, when a processing point itself does not represent high-probability impulsive noise, outputting the input signal as is without performing median filter processing is vital to avoid image degradation. Given the above, one approach for restoring images degraded by high-probability impulsive noise is to first determine whether or not a processing point represents impulsive noise, and then perform median filter processing only when it is determined that impulsive noise is present. Second, when performing median filter processing, the amount of impulsive noise adjacent to a processing point must be estimated, the size of the filter window appropriate to that amount must be set, and efforts to preserve the signal must be made. In the proposed method the authors were able to unify three types of fuzzy inference. In addition, the proposed method is superior at handling images degraded by impulsive noise over a probability range of 10 to 50% when the fuzzy sets are fixed by setting the fuzzy sets in the three fuzzy inferences appropriately. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(9): 43–57, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1118