AN IMPROVED IMAGE FUSION ALGORITHM BASED ON WAVELET TRANSFORMS USING PARTICLE OF SWARM OPTIMIZATION

Feature based image fusion is new area of research in the field of image fusion. The image fusion used lower content of image feature. The lower content of image feature such as color texture and dimension. The texture features are veryimportant component of image. The processing and extraction of texture feature used various transform function such as wavelet transform function, Gabor transform function and many more signal based transform function. In the process of image fusion involve two and more image for the process of fusion. The fused image still image pervious quality as well as new feature and area of improved by new and adopted reference image. In this paper, we proposed a feature based image fusion technique. The feature based optimization technique also used feature selection and feature optimization process. The feature selection and feature optimization used particle of swarm optimization technique. The particle of swarm optimization technique selects the optimal texture feature of both image original image and reference image. The original and reference image find the optimal feature sub set for the estimation of feature correlation.