1 July 2007 New adaptive vector filter using fuzzy metrics
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J. of Electronic Imaging, 16(3), 033007 (2007). doi:10.1117/1.2767335
Abstract
Classical nonlinear vector median-based filters are well-known methods for impulsive noise suppression in color images, but mostly they lack good detail-preserving ability. We use a class of fuzzy metrics to introduce a vector filter aimed at improving the detail-preserving ability of classical vector filters while effectively removing impulsive noise. The output of the proposed method is the pixel inside the filter window which maximizes the similarity in color and spatial closeness. The use of fuzzy metrics allows us to handle both criteria simultaneously. The filter is designed so that the importance of the spatial criterion can be adjusted. We show that the filter can adapt to the density of the contaminating noise by adjusting the spatial criterion importance. Classical and recent filters are used to assess the proposed filtering. The experimental results show that the proposed technique performs competitively.
Samuel Morillas, Valentin Gregori, Guillermo Peris-Fajarnes, Almanzor Sapena, "New adaptive vector filter using fuzzy metrics," Journal of Electronic Imaging 16(3), 033007 (1 July 2007). http://dx.doi.org/10.1117/1.2767335
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KEYWORDS
Digital filtering

Image filtering

Fuzzy logic

Nonlinear filtering

Optical filters

RGB color model

Distance measurement

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