A new neighborhood analysis hybrid vector filter (NAHVF) approach for impulse noise removal in color images is presented. First, a fuzzy decision rule using a semi-neighborhood set statistic ordered technique is used to detect impulse noise. Accordingly, two pixel sets, a noise-free neighborhood set and a partial relation noise-free neighborhood set, are defined. For a noisy pixel, one of three filters; a vector median filter within the noise-free neighborhood set, a similarity weighted mean filter within the partial relation noise-free neighborhood set, and a noise-free component spatial distance weighted mean filter, are selected to filter the noise. Each of these three filters is designed with different filtering strategies. One advantage of the proposed scheme is that the noise-free components of the pixel vector in the noise-free neighborhood set or partial relation noise-free neighborhood set are used to design the filter. This effectively reduces additional "filtering" noise into components that were noise-free before filtering. Another advantage is that the locations of correlated pixels in the same window and the correlations among different channel images are fully utilized for noise removal. Finally, experimental results show that the proposed method effectively removes impulse noise and preserves color information as well as image details. (C) 2018 Elsevier B.V. All rights reserved.
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