中北大学学报（自然科学版）2025，Vol.46Issue(5)：584-591,610,9.DOI:10.62756/jnuc.issn.1673-3193.2025.03.0003
基于类激活映射的红外与可见光图像融合方法
Method for Infrared and Visible Image Fusion Based on Class Activation Mapping
摘要
Abstract
To address the issues of fixed and monotonous information selection strategies in current image fusion algorithms,which lead to the loss of critical source image information and interference from invalid noise degrading fusion quality,this paper proposed an interpretable infrared and visible image fusion method based on Class Activation Mapping(CAM).By leveraging the CAM mechanism,class activation weights were derived from different source images,reflecting the network's attention to feature importance.These weights were utilized to assign channel-specific feature priorities,enabling weighted fusion of deep features to preserve richer salient targets,texture details,and critical information from source images while suppressing noise.Experimental results demonstrate that the proposed method outperformed most state-of-the-art algorithms on the TNO and RoadScene datasets.On the TNO dataset,it achieves superior information entropy(EN)and visual fidelity(VIF)scores of 7.327 2 and 0.692 7,respectively,significantly surpassing existing approaches.This indicates that the proposed method effectively retains key features of source images while exhibiting exceptional visual perception performance.关键词
图像融合/信息选择/类激活映射/权重分配/深度学习Key words
image fusion/information selection/class activation mapping/weight allocation/deep learning分类
信息技术与安全科学引用本文复制引用
完琦,秦品乐,曾建潮..基于类激活映射的红外与可见光图像融合方法[J].中北大学学报（自然科学版）,2025,46(5):584-591,610,9.基金项目
山西省科技重大专项计划(202101010101018) （202101010101018）
