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how to enhance color image using fuzzy logic

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sachit
sachit el 21 de Dic. de 2014
Comentada: Subhadeep Koley el 28 de Feb. de 2018
to convert the given RGB image of size P × Q into HSV and then calculate the histogram h(x) where x Є V. h(x) indicates the number of pixels in the image with intensity value x. Proposed method uses two intensification parameters M and K, which controls the degree at which the intensity value x has to be intensified. The control parameter M, M= sum(Xh(x))/sum(h(x)) & k=128
The parameter M divides the histogram h(x) into two classes C1([0,M-1])& C2([M,255]). The stretching of V component is performed based on two fuzzy membership values D1 and D2,Parameter M has a significant role in the computation of fuzzy membership values,
D1=1-(M-X)/M
the contrast enhanced or intensified value xe for class C1
xe=X + D1(X)K
D2=E-X/E-M
the contrast enhanced or intensified value xe for class C2
Xe = (XD2(X))+(E −D2(X)K))
The replacement of the old x values of the V component with the enhanced xe values will cause the V component to be stretched resulting in contrast and brightness enhanced component Ve. This enhanced achromatic information Ve can be combined with the preserved chromatic information (Hue and Saturation components) to obtain enhanced image HSVe which is finally converted to enhanced RGBe image.
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Walter Roberson
Walter Roberson el 31 de En. de 2018
Editada: Walter Roberson el 31 de En. de 2018
If the fis needs to examine the entire array to work out the histogram and the appropriate parameters, then use a fis with as many variables as (image length) times (image width) times (number of color planes).
Subhadeep Koley
Subhadeep Koley el 28 de Feb. de 2018
I have a 512*512 grayscale image to be given as the input of a FIS. What should be the no. of variables in the FIS.

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