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一个基于Res2Net和多尺度变压器的红外和可见图像融合网络

Binxi Tan¹, Bin Yang¹

¹College of Electrical Engineering, University of South China, Hengyang 421001, China.

Sensors (Basel, Switzerland)

|February 13, 2025

概括

本研究介绍了RMTFuse,这是一个用于红外和可见图像融合的新型深度学习网络. RMTFuse通过集成Res2Net和多尺度变压器模块来提高目标可见性和纹理细节,以获得更优质的特征表示.

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:

红外和可见图像的融合旨在将图像结合起来,以提高目标检测和纹理细节.
当前的深度学习融合方法通常依赖于卷积,限制了长距离的上下文信息表示.

研究的目的:

开发一个新的红外和可见图像融合网络,RMTFuse,克服卷积方法的局限性.
改善地方和全球上下文信息的表现,以获得更好的融合结果.

主要方法:

设计了RMTFuse,一个利用Res2Net进行局部特征提取 (LFE-RN) 的网络.
集成了一个多尺度变压器 (GFE-MT) 用于全球特征提取,包括一个变压器模块和一个全球特征集成模块 (GFIM).
采用预先训练的VGG-16网络进行多深度特征损失计算.

主要成果:

RMTFuse有效地使用Res2Net提取本地特征,并使用多尺度变压器捕获粗细的语义特征.
GFIM模块通过聚合层次特征来增强上下文特征表示.
实验表明,RMTFuse在主观和客观评估方面优于最先进的方法.

结论:

通过有效利用本地和全球特征提取,RMTFuse在红外和可见图像融合方面取得了重大进展.
拟议的网络在突出显示红外线目标方面实现了卓越的性能,同时保持了详细的纹理.

关键词:

变压器变压器变压器深度学习是一种深度学习.图像融合图像融合图像融合红外图像中的红外图像.多个尺度的特征是多个尺度的特征.

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