Medical image fusion based on joint sparse method

In this paper, a novel joint image fusion algorithm which is the hybrid of Orthogonal Matching Pursuit (OMP) and Principal Component Analysis (PCA) is proposed to properly utilize the advantages and to overcome the disadvantages of both OMP and PCA methods. Firstly, common and innovative images are extracted from the source images. Secondly, sparse PCA method is employed to fuse the information of innovative features. Then weighted average fusion is used to fuse the sparse PCA result with the common feature thereby preserving the edge information and high spatial resolution. We demonstrate this methodology on medical images from different sources and the experimental results proves the robustness of the proposed method.