Algorithm for Image Fusion via Gradient Correlation and Difference Statistics

摘要

In order to overcome the shortcoming of traditional image fusion based on discrete wavelet transform (DWT), a novel image fusion algorithm based on gradient correlation and difference statistics is proposed in this paper. The source images are decomposed into low-frequency coefficients and high-frequency coefficients by DWT: the former are fused by a local gradient correlation based scheme to extract the local feature information in source images; the latter are fused by a neighbor difference statistics based scheme to reserve the conspicuous edge information. Finally, the fused image is reconstructed by inverse DWT. Experimental results show that the proposed method performs better than other methods in reserving details.