通过残余互动变压器和交叉注意力融合的红外和可见图像融合
Liquan Zhao1, Chen Ke1, Yanfei Jia2
1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一个新的红外和可见图像融合网络,使用剩余的交互式变压器和交叉注意力融合. 该方法增强了融合图像细节和热目标对比度,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 基于深度学习的红外和可见图像融合方法难以整合全球和本地特征.
- 这种限制导致纹理细节差,并使合图像的对比度低,特别是在热目标.
研究的目的:
- 提出一个先进的红外和可见图像融合网络.
- 改进全球和本地信息之间的依赖关系的提取,以提高融合结果.
主要方法:
- 一个新的网络,包含一个残余密集模块,用于浅层特征提取.
- 使用剩余交互式变压器提取和交互全球和本地特征.
- 采用交叉注意力融合模块来实现有效的功能地图集成和最终输出的图像重建网络.
主要成果:
- 拟议的方法显著提高了合图像中可见的纹理细节的清晰度.
- 实现了红外热目标的增强对比度和可见性.
- 在RoadScene,TNO和M3FD数据集上的实验结果表明,与其他九种融合方法相比,其性能优越.
结论:
- 开发的红外和可见图像融合网络有效地解决了现有方法的局限性.
- 剩余交互式变压器和交叉注意力融合的整合导致了卓越的融合性能,提供更丰富的细节和更好的对比度.
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