液晶与显示2025，Vol.40Issue(12)：1840-1852,13.DOI:10.37188/CJLCD.2025-0195
基于全局双组注意力的红外与可见光图像融合
Infrared and visible image fusion based on global dual-group attention
摘要
Abstract
In complex scenarios,infrared and visible image fusion models often struggle to fully extract the characteristics of overall macro-structures(from infrared images)and local micro-details(from visible images),as well as to achieve synergy between these elements,which degrades fusion quality.To address this problem,this paper proposes a collaborative fusion principle based on scale specialization and designs a new fusion model based on an autoencoder architecture.The encoder and decoder of the model adopt a convolutional neural network(CNN)architecture.The model utilizes the global dual-group attention mechanism:after grouping feature maps by length and width to extract information,the generated inter-group channel attention map can achieve weighting of the feature maps,thereby generating new feature maps containing more large-scale global structural information.The model utilises a convolution mechanism with multi-scale pooling and dilation,using receptive fields of different sizes and implementing global average and median pooling operations,to extract small-scale local features in the image.The model utilizes a decoder to integrate the large-scale structure and small-scale details of densely connected layers and skip connections,enabling them to synergistically fuse and reconstruct the fused image.The experimental results demonstrate that,on the MSRS and TNO datasets,compared to the best results of other methods,the information entropy,mean gradient,and edge intensity were improved by 0.95%,6.28%,and 6.19%,and then by 1.75%,13.51%,and 11.75%respectively.Spatial frequency increased by 4.61%on the MSRS dataset,second only to the MDLSR-RFM method on the TNO dataset.These results validate the improvement in the quality of merged images in complex scenarios,as well as the increased robustness and generalization of the model.关键词
红外与可见光图像融合/图像增强/全局双组注意力/空洞卷积Key words
infrared and visible image fusion/image enhancement/global dual-group attention/dilated convolution分类
信息技术与安全科学引用本文复制引用
ZHAO Yang,YANG Wengui,GAO Cuiyun..基于全局双组注意力的红外与可见光图像融合[J].液晶与显示,2025,40(12):1840-1852,13.基金项目
安徽省高校自然科学基金(No.KJ2019A0765) （No.KJ2019A0765）
安徽省古建筑智能感知与高维建模国际联合研究中心主任基金(No.GJZZX2024ZR02)Supported by Natural Science Foundation of Anhui Province Higher Education Institutions(No.KJ2019A0765) （No.GJZZX2024ZR02）
Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling(No.GJZZX2024ZR02) （No.GJZZX2024ZR02）
