关键词: 多模态融合, 稀疏表示, 拉普拉斯正则化

Abstract: Fusion of thermal and visible images has a large research and application value due to their complementary benefits, which can overcome shortcomings of single modality under certain conditions. This paper adopts a sparse representation based algorithm to integrate thermal and visible information. Firstly, a relative complete dictionary is learned by some image samples. Secondly, given an image pair, this paper represents them on the learned dictionary by the improved sparse representation model, in which the Laplacian constraints on reconstructed coefficients are employed to improve its robustness. Then, the two modal images are integrated based on the constructed coefficients. Finally, extensive experiments on the public images and the collected images suggest that the method proposed in this paper can effectively fuse the information of two modalities.

Key words: multi-modal fusion, sparse representation, Laplacian regularization