Summary
An image may be subject to noise from several sources. The presence of noise in an image can affect the accuracy of the results considerably. Because of its wide applicability to image filtering, several fuzzy filter methods have been proposed. In this chapter, a survey of different design techniques for fuzzy filters is presented. Six filters are investigated: multipass fuzzy, fuzzy multilevel median, histogram adaptive, fuzzy vector rank, fuzzy vector rational median, and fuzzy credibility color filters. An effort is made to evaluate the performance of the filters using criteria such as: mean average error (MAE), mean square error (MSE), normalized mean square error (NMSE), signal to noise error ratio (SNR) and mean chromaticity error (MCRE). The evaluation is based on some real world images.
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Forero-Vargas, M.G., Delgado-Rangel, L.J. (2003). Fuzzy Filters for Noise Removal. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Van De Ville, D. (eds) Fuzzy Filters for Image Processing. Studies in Fuzziness and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36420-7_1
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DOI: https://doi.org/10.1007/978-3-540-36420-7_1
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