Fuzzy Rule Based Bifurcation of Noise
Abstract
Muzzle patterns of animals are uneven features of their skin surface. They are unique for every animal like finger prints of human. Hence these muzzle patterns can be used to identify cattle. Noise is any unwanted component in an image. It is important to segregate noise in the of pattern recognition. This paper propound a method for the segregation of salt and pepper noise based on mathematical ways using fuzzy rules from muzzle images. The proposed Fuzzy operator consists of two modules viz. Detection module and Adaptation module. A triangular shaped fuzzy set described by a two parameter membership function is used for fuzzy reasoning. 13 fuzzy rules are applied for each pixel element and an output is induced in detection module. In adaptation module is further reduced and it is added with input pixel value to get output pixel value. This method is able to perform a very strong noise cancellation while preserving muzzle image details. Various non-linear filters such as Median Filter, SDROM Filter, PSM Filter is compared with Fuzzy Filter and is getting better results in terms of PSNR values and SSIM Values.
Keywords
Muzzle Print, Impulse Noise, Fuzzy Operator, Fuzzy Reasoning, Membership function, PSM Filter, ]SDROM Filter, PSNR, SSIM Index
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