A Novel Image Fusion Method Using Non-subsampled Shearlet Transform

摘要

As a novel MGA (Multiscale Geometric Analysis) tool, shearlet is equipped with a rich mathematical structure similar to wavelet. In this paper, a novel image fusion method using Non-subsampled Shearlet Transform is proposed. First, the source images are decomposed into low-pass and high-pass subbands using NSST. Second, the high-pass subbands coefficients of the images are fused according to the average gradient. Third, the low-pass subbands coefficients of the images are fused by the weighted regional entropy. Finally, the image is reconstructed by the inverse non-subsampled shearlet transform. In the method, two sets of source images and five objective parameters are used to test the algorithm. The experimental results show that the proposed method is better than the conventional DWT-based and NSCT-based methods.