IMAGE FUSION USING HIGHER ORDER SINGULAR VALUE DECOMPOSITION

The goal of image fusion is to combine relevant information from two or more source images into one single image such that the single image contains as much information from all the source images as possible. There are many image fusion methods. This paper present some of the image fusion techniques for image fusion and propose novel higher order singular value decomposition (HOSVD) based image fusion algorithm. Image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor. The sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch, and a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result.