A novel image fusion rule based on Structure Similarity indices

Abstract

A novel image fusion rule named “variance-choosemax” based on Structure Similarity Index is proposed in this paper. Firstly, the sparse representation of source image patches are acquired through bases training algorithm K-SVD and pursuit algorithm Orthogonal Matching Pursuit. Then, we group image patches into relevant patches and independent patches according to the Structure Similarity Index of each patch pair. Finally, we fuse the corresponding sparse coefficients of relevant patches and independent patches with “coefficient-choose-max” rule and a new fusion rule named “variance-choose-max” respectively. According to the experiments, our proposed method gains a good performance in visual quality of fused image and also in objective metric.