发表：Computer Vision and Image Understanding

作者：Chengfang Zhang, Haoyue Li, Ziliang Feng, Sidi He

方向：电子数据取证与分析

摘要：Joint sparse coding (JSC) and coupled dictionary learning (CDL) have been successful in visible and infrared image fusion tasks. However, JSC fusion methods using a single dictionary cannot perfectly characterize different modal signals. The CDL-based fusion methods ignore the role of high-frequency components in texture preservation and sharpness. In this study, we design fusion rules while considering the uniqueness and connection of different feature spaces to alleviate this issue. In the proposed fusion method, coupled dictionaries are first used to establish the relationship between infrared and visible images. Then, we use joint sparse coding and coupled dictionaries to calculate sparse coefficients via the joint sparse model. Finally, the improved coefficient fusion strategy realizes the conversion and synthesis between different spatial images. Experimental results on the TNO and RoadScene datasets show that the proposed method achieves promising results in terms of objective and subjective performance compared with state-of-the-art algorithms.