红外技术2026，Vol.48Issue(1)：27-35,9.
一种联合学习局部与全局特征的红外和可见光图像融合方法
Fusion Approach for Joint Learning of Local and Global Features in Infrared and Visible Images
摘要
Abstract
Existing infrared and visible image fusion approaches cannot fully integrate local and global feature representations,resulting in bias and smoothness in the fused image.Therefore,in this study,we propose a fusion approach for jointly learning local and global features,namely JLFuse.First,a convolution transformer is introduced based on traditional convolution sampling to enhance the modeling ability of the global features.Second,a fusion strategy(JLFN),based on spatially separable self-attention,is designed using locally grouped self-attention and global sub-sampled attention alternately guided transformer modules to achieve joint learning of local and global fusion features.Finally,the pyramid design principle is adopted to obtain multiscale features and enhance the local propagation.Experimental results on the TNO and RoadScene datasets show that the proposed approach outperforms six advanced fusion approaches in multiple objective evaluation metrics.Subjectively,the fused images are more consistent with human visual preferences.关键词
图像融合/红外图像/可见光图像/联合学习Key words
image fusion/infrared image/visible image/joint learning分类
信息技术与安全科学引用本文复制引用
朱达荣,吴晗,汪方斌,龚雪..一种联合学习局部与全局特征的红外和可见光图像融合方法[J].红外技术,2026,48(1):27-35,9.基金项目
国家自然科学基金(61871002) （61871002）
安徽省自然科学基金(2008085UD09,1808085ME125) （2008085UD09,1808085ME125）
安徽省教育厅高校自然科学重点项目(KJ2020A0487,KJ2019A0795). （KJ2020A0487,KJ2019A0795）
