HDCTfusion:基于CNN和变压器的混合双分支网络,用于红外和可见图像融合
Wenqing Wang1,2, Lingzhou Li1, Yifei Yang1
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种新的双分支融合网络,用于红外和可见图像融合. 新方法增强了特征提取,与现有的算法相比,提高了融合图像质量和细节.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 红外和可见图像融合旨在整合两种模式的互补信息.
- 现有的融合方法往往难以有效地提取本地和全球图像特征,导致信息丢失.
研究的目的:
- 提出一种新的双分支融合网络,用于红外和可见图像融合.
- 为了增强功能提取能力,以生成具有丰富细节和对比度的融合图像.
主要方法:
- 一个双分支网络,结合了卷积神经网络 (CNN) 和变压器架构.
- 使用基于CNN的局部特征提取模块,具有剩余梯度和协调注意力机制.
- 采用基于变压器的全球特征提取模块来捕获上下文信息.
主要成果:
- 拟议的网络有效地从红外和可见图像中提取本地和全球特征.
- 在各种数据集上的实验结果表明,与现有的先进的聚变算法相比,其性能优越.
- 融合图像显示了改进的目标信息,细节和对比度.
结论:
- 双分支融合网络显著推进了红外和可见图像的融合.
- 整合CNN和变压器为全面的特征提取提供了强大的方法.
- 该方法为需要高质量的化图像的应用提供了有前途的解决方案.
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