Add weighted algorithm based on the PICA and RBF neural network for image fusion

The paper has discussed the image fusion using add weighted algorithm based on the pixel intensity clustering algorithm (PICA) and radial basis function (RBF) neural network thresholding method. It is a comparative study when we apply add weighted fusion algorithm with and without applying PICA and RBF neural network thresholding algorithm. The PICA and RBF neural network are applied to grayscale IR image to extract the object by the determination of threshold value for the IR image. After applying the PICA and RBF we will have the IR image where object and background are separated. Now we fuse this grayscale IR image where the object is separated from the background with the visible image at different weights and try to find out the proper weight for the fusion. We also fuse the raw IR image with the visible image to compare with our result. The raw IR image means an image where we do not apply any PICA and RBF neural network algorithm. From the comparison, we can have the object with better resolution background when we fuse the IR image after applying PICA and RBF.