[1]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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Infrared and visible aerial image fusion via dynamic environmental brightness perception

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1249-1259 Column: 学术论文—机器感知与模式识别 Public date: 2026-09-05
Title:
Infrared and visible aerial image fusion via dynamic environmental brightness perception
Author(s):
TIAN Feng1; 2;  HAN Zhuohan1; 2;  LIU Fang1; 2;  ZHANG Yan1; 2;  XIE Hongtao1; 2;  GAO Fan1; 2;  HUANG Bin1; 2;  LIU Zongbao2
1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;
2. Heilongjiang Provincial Key Laboratory of Oil Big Data & Intelligent Analysis, Daqing 163318, China
Keywords:
image fusion; unmanned aerial vehicles; environmental brightness adaptation; infrared image; high-frequency information enhancement; feature fusion; cross attention; object detection
CLC:
TP391.4
DOI:
10.11992/tis.202509028
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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