红外技术2026，Vol.48Issue(3)：305-314,10.
基于自适应增强损失和多尺度空洞卷积的夜间红外与可见光图像融合
Nighttime Infrared and Visible Image Fusion Based on Adaptive Enhancement Loss and Multi-Scale Dilated Convolution
摘要
Abstract
Most existing infrared and visible image fusion methods are designed for well-lit environments,resulting in issues such as low contrast,loss of textural details,and color distortion when applied to nighttime scenarios.To address this issue,this study proposes a nighttime infrared and visible image fusion method based on adaptive enhancement loss and multi-scale dilated convolution.First,an illumination adjustment network was designed using adaptive enhancement loss,which guides the adaptive enhancement curve to enhance the brightness and color information of nighttime visible images.Second,a multi-scale dilated convolution module was designed for the dual-branch feature-extraction structure of the fusion network to better capture multi-scale features and contextual information.Finally,a joint training approach was used to train both the illumination adjustment and fusion networks,achieving coupling of the underlying feature representations for both the enhancement and fusion tasks.Comparative experiments were conducted on the LLVIP,MSRS,and TNO datasets using seven representative fusion algorithms.The experimental results show that the proposed method outperformed current mainstream algorithms in both subjective and objective evaluations,significantly improving the contrast and clarity of the fused images in low-light scenarios while preserving more textural details and color information.关键词
红外与可见光图像融合/微光增强/夜间场景Key words
infrared and visible image fusion/low-light enhancement/nighttime分类
信息技术与安全科学引用本文复制引用
张永生,李泽阳..基于自适应增强损失和多尺度空洞卷积的夜间红外与可见光图像融合[J].红外技术,2026,48(3):305-314,10.基金项目
吉林省自然科学基金项目(YDZJ202401633ZYTS). （YDZJ202401633ZYTS）
