计算机与现代化Issue(4)：89-95,102,8.DOI:10.3969/j.issn.1006-2475.2025.04.014
基于孪生轴向注意力与双鉴别器生成对抗网络的红外可见光图像融合
Infrared and Visible Image Fusion Based on Twin Axial-attention and Dual-discriminator Generative Adversarial Network
摘要
Abstract
For the same scene,the fused image of infrared and visible can preserve the thermal radiation information of the fore-ground target and the background texture details at the same time,and the description is more comprehensive and accurate.How-ever,many classical fusion algorithms based on deep learning usually have the defects of insufficient information retention and unbalanced feature fusion.To solve these problems,an image fusion algorithm based on twin axial-attention and dual-discriminator generating adversarial network is proposed.The generator uses a double-dense convolutional network as a multi-scale feature extractor and introduces spatially enhanced branch and twin axial attention to capture local information and long-range dependencies.The adversarial game between the dual discriminator and the generator is constructed,and the retention de-gree of differential features is balanced by restricting the similarity between the two source images and the fusion image.The per-ceptual loss function based on pre-trained VGG19 can overcome the problem of losing high-level features such as semantic-level features.The experimental results on the TNO dataset show that the proposed method achieves prominent fusion results with clear textures and has significant improvements in both subjective and objective evaluation metrics compared to other classical al-gorithms,demonstrating its advancement.关键词
图像融合/生成对抗网络/轴向注意力/双鉴别器/密集连接网络Key words
image fusion/generative adversarial networks/axial-attention module/dual-discriminators/DenseNet分类
信息技术与安全科学引用本文复制引用
王丽丹,赵怀慈,潘多涛,房建,袁德成..基于孪生轴向注意力与双鉴别器生成对抗网络的红外可见光图像融合[J].计算机与现代化,2025,(4):89-95,102,8.基金项目
辽宁省自然科学基金资助项目(2023010411-JH3/101) （2023010411-JH3/101）
