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TGLFusion:用于红外和可见图像的温度引导轻量化融合方法

Bao Yan¹, Longjie Zhao¹, Kehua Miao¹

¹School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.

Sensors (Basel, Switzerland)

|March 28, 2024

概括

这项研究引入了一种新的红外和可见图像融合方法,该方法解决了信息失衡和计算复杂性的问题. 新方法增强了温度和边缘细节,以提高精度,特别是在边缘设备上.

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对于取代手动观测至关重要,特别是在实时应用的边缘设备上.
现有的聚变方法在红外和可见光谱之间的信息不平衡中扎,经常忽视关键温度和边缘纹理细节.
计算复杂性阻碍了在资源有限的边缘设备上部署当前的融合技术.

研究的目的:

开发一种高效,轻量级的图像融合方法,克服信息不平衡问题.
为了在合图像中加强对温度和边缘纹理信息的强调.
为了实现适应性聚变重量分配,以提高边缘设备的性能.

主要方法:

提出了一种计算红外像素值分布的新方法,以确定自适应融合重量.
引入了一个重量分配机制和一个轻量级的MobileBlock与多光谱信息补充模块.
使用温度-颜色-感知损失函数用于训练期间适应性体重调整.

主要成果:

与现有的主流聚变技术相比,拟议的方法显示出更高的性能.
实现温度和边缘纹理信息的增强突出显示,减少误解.
该模型在计算上高效,适合在边缘设备上部署.

结论:

开发的融合方法有效地解决了红外和可见图像融合中的信息不平衡和计算挑战.
这些创新带来了加强的核聚变能力,轻量化设计和基本信息补偿.
这种方法对现实世界的应用有很大的希望,特别是在电力设备监控方面.

关键词:

深度学习是一种深度学习.电力设备电力设备电力设备图像融合图像融合图像融合红外和可见传感器图像.轻量级的模型轻量级的模型.

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