Multi-focus Image Fusion Based on Super-resolution and Group Sparse Representation

A multi-focus image fusion method based on super-resolution combined with group sparse representation model is proposed. First, the bicubic interpolation method is used to enhance the resolution of the source image and the source multi-focus image information. Then, the adaptive sparse representation learning dictionary is used to learn the image blocks without obvious dominant direction and specific dominant direction respectively. The sparse coefficient representation of the source multi focus image is conducted by the group sparse representation model. Finally, the maximum l(1) norm is used to select the final representation coefficient vector. The experimental results show that the proposed method restrains the shortcomings of low spatial resolution and blurring that are easy to appear in multi-focus image fusion, and has better contrast and sharpness. Subjective visual effects and objective indicators show that the proposed method has certain advantages over traditional multi-focus image fusion methods, especially in the mutual information index of the three sets of image fusion results leading 0.37, 0.38 and 0.32 respectively.