FECFusion:基于快速边缘卷积的红外和可见图像融合网络
Zhaoyu Chen1, Hongbo Fan2, Meiyan Ma1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
这项研究介绍了FECFusion,这是用于红外和可见图像融合的新算法. 它以更少的计算资源实现了卓越的融合性能,有效地增强了场景细节.
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:
- 红外和可见图像的融合旨在将互补的信息结合起来,以增强场景细节.
- 现有的深度学习方法面临着性能-资源不平衡和无效的异模式特征融合的挑战.
研究的目的:
- 开发一种新的红外和可见图像融合算法 (FECFusion),平衡融合性能和计算成本.
- 为了改善纹理和异模特征的提取和融合.
主要方法:
- 利用结构重新参数化边缘卷积 (RECB) 与嵌入边缘运算符来增强纹理特征提取.
- 采用注意力融合模块 (AFM) 来融合独特和公共的异模特征.
- 优化了网络,使用结构重组参数化来实现类似VGG的架构,从而提高了推断速度.
主要成果:
- 与MSRS,TNO和M3FD数据集上的七个先进算法相比,FECFusion在多个评估指标上表现出卓越的性能.
- 算法实现了更好的视觉效果和更丰富的场景细节信息.
- 与现有的方法相比,FECFusion消耗的计算资源较少.
结论:
- 拟议的FECFusion算法有效地解决了当前深度学习融合方法的局限性.
- 它为红外和可见图像融合提供了高效和高性能解决方案.
- 类似于VGG的架构可以提高融合速度,而不会影响性能.
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