mage fusion based on fuzzy sets
The fuzzy logic approach is widely used in image process-ing. The fuzzy logic gives decision rules and fusion motivation for image fusion [17]. the two inputs images are converted into membership values based on a set of predefined MFs, where the degree of membership of each input pixel to a fuzzy set is determined. Then, the fusion operators are applied to the fuzzified images. The fusion results are then converted back into pixel values using defuzzification.
1) Fuzzy sets: The fuzzy sets are used to describe the gray levels of the input images. we have two inputs and one output. the two inputs are ; the first input is the Pan image and the second input is the first principal component( PC
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) of the MS images. the output is fused image. Every input image consists of pixels. each pixel value has the range [0,255], so they have
256 gray levels. Divide the 256 gray levels into the five fuzzy sets (VL, L, M, H, VH). we used the same fuzzy set for inputs and outputs because of the inputs and the outputs are gray image which have 256 gray levels.
2) Membership Functions : The membership function is used to demonstrate the distribution and clustering of the pixel values, and allows the best fusion operators and the decision rules for image fusion. The five fuzzy sets (VL, L, M, H, VH) in fusion technique have five membership function states as follows: VL : Represents the very low gray level
L : Represents the low gray level
M :Represents the meduim gray level
H : Represents the high gray level
VH :Represents the very high gray level.
The Triangular function is suitable for fuzzify the inputs and the outputs, see figure 2 ; because it is simplest and it became clear to us that the fused result...
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... which has Z= m1*n1 entries.
6: Make a fis (Fuzzy) file, which has two input images, the number of member ship function is 5 and type of membership functions for both the input images and output image is trimf. Input images are ranging from 0 to 255.
7: Make rules for input images, the number of rules is 25, which resolve the two antecedents to a single number from 0 to 255. For num=l to C in steps of one, apply fuzzification using he rules developed above on the cor-responding pixel values of the input images which gives a fuzzy set represented by a membership function and results in output image in column format.
8: Make PCA inverse to get the fused image from column format 9: Output: fused image
D. Reconstruction Final Image
Finally the inverse of the PCA is applied to obtain the final image fusion and measures the quality of the image used by different metrics.
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Key Words; Artificial Intelligence, Multiple Intelligence, Fuzzy Logic, Fuzzy Logic Toolbox, Vocational Guidance, Decision Making
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