Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (10): 68-76.doi: 10.11896/jsjkx.260500078

• Large Models and Multimodal Intelligent Perception • Previous Articles     Next Articles

HLDNet:High-Low Frequency Dual-stream Decoupling Network for Infrared and Visible ImageFusion

LI Ye, WANG Qi, XIA Jingyuan, HUANG Xinghua, JIANG Weidong   

  1. College of Electronic Science,National University of Defense Technology,Changsha 410073, China
  • Received:2026-05-14 Revised:2026-08-03 Published:2026-10-13
  • About author:LI Ye,born in 2001,Ph.D.His main research interests include multi-modal image fusion and deep learning.
    XIA Jingyuan,born in 1991,associate professor.His main research interests include optimization,AIGC and low le-vel vision.
  • Supported by:
    National Natural Science Foundation of China(62531026) and Basic Research Program for Young Students of the National Natural Science Foundation of China(625B2180).

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Abstract: Infrared and visible image fusion aims to fuse the sharp contour structures of infrared images and the rich texture information of visible images,which can be used in scenarios such as medical diagnosis and autonomous driving.Although existing deep fusion methods have gradually incorporated frequency domain transformation or frequency attention,most of them still embed frequency domain information as an auxiliary enhancement item into a unified backbone,lacking explicit separation and diffe-rentiated modeling of low-frequency structures and high-frequency details.Therefore,this paper proposes a high-low frequency dual-stream decoupling network(HLDNet) for infrared and visible image fusion.Firstly,the network decomposes infrared and visible images into low-frequency structural components and high-frequency detail components using Gaussian low-pass and residual high-pass filters.Subsequently,it constructs two heterogeneous branches,which are responsible for modeling the structural information and enhancing local edge textures,respectively.In the fusion stage,the low-frequency stream employs cross-modal guided attention and gating mechanisms to highlight thermal targets and scene structures,while the high-frequency stream utilizes a differential-driven detail mask to enhance boundaries and textures.Finally,a cross stream consistency module is employed to enhance the structural information and salient regions using low-frequency spatial priors,and to enhance image detail textures using high-frequency boundary information.Experimental comparisons with other advanced methods show that the proposed method achieves MI and Qabf of 4.21 and 0.70 on the ROADSCENE dataset,EN and SF of 7.30 and 17.99 on the M3FD dataset,and VIF and Qabf of 1.12 and 0.75 on the MSRS dataset,respectively,validating the effectiveness of the proposed method in edge enhancement and detail fidelity.

Key words: Multi-modal images, Multi-frequency fusion, High-low frequency decoupling, Cross-attention mechanism, Adaptive reconstruction, Infrared and visible image fusion

CLC Number: 

  • TP391
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