Fuzzy Set Based Image Fusion The fuzzy logic approach is widely used in image processing. Fuzzy logic provides decision rules and motivation for image fusion [17]. the two input 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, fusion operators are applied to the fuzzified images. The fusion results are then converted back to pixel values ​​using defuzzification.1) Fuzzy Sets: Fuzzy sets are used to describe the gray levels of the input images. we have two entrances and one exit. the two inputs are ; the first input is the Pan image and the second input is the first principal component (PC1) of the MS images. the output is a merged image. Each input image is made up 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 input and output because the inputs and outputs are gray images which have 256 gray levels.2) Membership Functions: Membership function is used to demonstrate the distribution and clustering of pixel values ​​and allows the best fusion operators and decision rules for image fusion. The five fuzzy sets (VL, L, M, H, VH) in the 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 medium gray level H: Represents the high gray level VH: Represents the very high gray level. The Triangular function is suitable for fuzzifying inputs and outputs, see figure 2; because it is simpler and it became clear to us that the result of the merger...... half of the sheet...... which has Z= m1*n1 entries.6: Create a fis file (Fuzzy), which has two input images, the membership function number is 5, and the type of membership functions for both input images and output image is trimf. Input images range from 0 to 255.7: Create rules for the input images, the number of rules is 25, which resolve the two antecedents to a single number from 0 to 255. For num=the C in steps of one , apply fuzzification using The rules developed above on the corresponding pixel values ​​of the input images give a fuzzy set represented by a membership function and result in an output image in column format. 8: Make inverse PCA to get fused image from column format 9: Output : fused imageD. Final image reconstructionFinally the inverse of PCA is applied to obtain the final image fusion and measure the image quality used by different metrics.I