红外技术2024，Vol.46Issue(7)：765-774,10.
基于多尺度及多头注意力的红外与可见光图像融合
Infrared and Visible Images Fusion Method Based on Multi-Scale Features and Multi-head Attention
摘要
Abstract
To address the challenges of detail loss and the imbalance between visual detail features and infrared(IR)target features in fused infrared and visible images,this study proposes a fusion method combining multiscale feature fusion and efficient multi-head self-attention(EMSA).The method includes several key steps.1)Multiscale coding network:It utilizes a multiscale coding network to extract multilevel features,enhancing the descriptive capability of the scene.2)Fusion strategy:It combines transformer-based EMSA with dense residual blocks to address the imbalance between local details and overall structure in the fusion process.3)Nested-connection based decoding network:It takes the multilevel fusion map and feeds it into a nested-connection based decoding network to reconstruct the fused result,emphasizing prominent IR targets and rich scene details.Extensive experiments on the TNO and M3FD public datasets demonstrate the efficacy of the proposed method.It achieves superior results in both quantitative metrics and visual comparisons.Specifically,the proposed method excels in targeted detection tasks,demonstrating state-of-the-art performance.This approach not only enhances the fusion quality by effectively preserving detailed information and balancing visual and IR features but also establishes a benchmark in the field of infrared and visible image fusion.关键词
图像融合/红外与可见光图像/多尺度特征/多头自注意力/TransformerKey words
image fusion/visible and infrared images/multi-scale features/multi-head self-attention/transformer分类
计算机与自动化引用本文复制引用
李秋恒,邓豪,刘桂华,庞忠祥,唐雪,赵俊琴,卢梦圆..基于多尺度及多头注意力的红外与可见光图像融合[J].红外技术,2024,46(7):765-774,10.基金项目
装备预先研究共用技术项目(50927010302). （50927010302）
