Infrared and Visible Image Fusion via Joint Low-rank and Sparse Decomposition

In order to further improve the detail information and overall contrast of the fused images and reduce artifacts and noises, an infrared and visible image fusion method based on joint low-rank and sparse decomposition was proposed by considering the correlation between infrared and visible images. First, infrared and visible images are jointly decomposed into common low-rank component, specific low-rank components and specific sparse components by using the joint low-rank and sparse decomposition method. Second, the nonsubsampled shearlet transform-based fusion method is performed on the specific low-rank components. Third, the specific sparse components are fused by adopting regional energy fusion rule. Finally, the fused image is obtained by integrating the common low-rank component, the fused specific low-rank component and the fused specific sparse component. The experiments conducted on the Nato-camp、Bristol Eden Project and TNO publicly test data sets are used to test the performance of the proposed algorithm. The experimental results demonstrate that the proposed method can effectively extract the target information of infrared image and retain the background of visible image compared with other nine fusion methods. Meanwhile, the values of the objective evaluation metrics such as entropy, mutual information, standard deviation, visual information fidelity, the sum of the correlations of differences and Qy are obviously better than those of the comparison methods.