[1]田枫,韩卓翰,刘芳,等.基于环境亮度动态感知的红外与可见光航拍图像融合方法[J].智能系统学报,2026,21(5):1249-1259.[doi:10.11992/tis.202509028]
TIAN Feng,HAN Zhuohan,LIU Fang,et al.Infrared and visible aerial image fusion via dynamic environmental brightness perception[J].CAAI transactions on intelligent systems,2026,21(5):1249-1259.[doi:10.11992/tis.202509028]
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TIAN Feng,HAN Zhuohan,LIU Fang,et al.Infrared and visible aerial image fusion via dynamic environmental brightness perception[J].CAAI transactions on intelligent systems,2026,21(5):1249-1259.[doi:10.11992/tis.202509028]
基于环境亮度动态感知的红外与可见光航拍图像融合方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第5期
页码:
1249-1259
栏目:
学术论文—机器感知与模式识别
出版日期:
2026-09-05
- Title:
- Infrared and visible aerial image fusion via dynamic environmental brightness perception
- Keywords:
- image fusion; unmanned aerial vehicles; environmental brightness adaptation; infrared image; high-frequency information enhancement; feature fusion; cross attention; object detection
- 分类号:
- TP391.4
- 摘要:
- 为克服现有红外与可见光图像融合方法在无人机航拍环境下的亮度自适应融合及互补特征挖掘不充分的问题，提出一种基于环境亮度动态感知的红外与可见光航拍图像融合方法。设计环境亮度感知模块，实现可见光特征的动态重组，减轻环境亮度变化对网络学习的干扰。构建高低频信息协同注意力，增强模型对红外图像边缘细节特征的捕捉能力，突出融合图像中的显著目标。设计离散小波交叉注意力块，通过让两种模态交叉关注，实现红外与可见光图像互补特征的高效融合。在公开数据集上使用多种评价指标对所提模型进行实验验证，实验结果表明，所提方法能够在无人机航拍环境下实现红外与可见光图像亮度自适应融合，并在定性和定量评价上取得了较好的融合效果，此外，通过实验证明，所提方法生成的融合图像能有效适用于无人机对地目标检测任务。
- Abstract:
- To address the issues of inadequate brightness-adaptive fusion and insufficient mining of complementary features in existing infrared and visible image fusion methods under UAV aerial conditions, this paper proposes a fusion method based on dynamic environmental brightness perception. First, an environmental brightness perception module is designed to dynamically reorganize visible features, reducing the interference caused by varying illumination conditions on network learning. Second, a cooperative high- and low-frequency attention mechanism is constructed to enhance the model’s ability to capture edge details from infrared images and highlight salient targets in the fused results. Finally, a discrete wavelet cross-attention block is introduced to facilitate efficient fusion of complementary features through cross-modal interaction. Extensive experiments on public datasets with multiple evaluation metrics demonstrate that the proposed method achieves adaptive brightness fusion of infrared and visible aerial images under UAV scenarios, yielding superior qualitative and quantitative results. Moreover, it is verified that the fused images generated by our method can effectively enhance ground target detection in UAV applications.
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备注/Memo
收稿日期:2025-9-17。
基金项目:黑龙江省科技创新基地项目(JD24A009)；东北石油大学特色科研团队项目(2023TSTD-04).
作者简介:田枫，教授，博士生导师，博士，东北石油大学计算机与信息技术学院院长，主要研究方向为智能油气地质、计算机视觉、智能数据分析处理。E-mail：tianfeng1980@163.com。;韩卓翰，硕士研究生，主要研究方向为低空视觉、多模态图像融合。E-mail：2869060778@qq.com。;刘芳，副教授，博士，主要研究方向为智能油气地质、智慧教育、多媒体与现代教育技术、计算机视觉。获黑龙江省科技进步二等奖1项、大庆市科技进步二等奖1项，主持和参与国家自然基金项目、黑龙江省自然基金项目、局级项目多项，发表学术论文 20余篇。E-mail：lfliufang1983@126.com。
通讯作者:刘芳. E-mail：lfliufang1983@126.com
更新日期/Last Update:
2026-09-05