SFPFusion:一个改进的视觉转换器,结合了超级特征注意力和波形导向聚合,用于红外和可见图像的融合
Hui Li1, Yongbiao Xiao1, Chunyang Cheng1
1International Joint Laboratory on Artificial Intelligence of Jiangsu Province, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了SFPFusion,这是一个新的图像融合网络,可以增强细节提取. 它有效地结合了全球和多尺度的功能,以获得卓越的红外和可见图像融合性能.
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:
- 卷积神经网络 (CNN) 在局部特征提取方面表现出色,但受体场有限.
- 变压器架构捕捉了全球特征,但往往忽视了关键的图像细节.
- 当前的图像融合方法难以平衡全球特征提取与细节增强.
研究的目的:
- 开发一种新的图像融合网络,SFPFusion,可以提高细节的保存.
- 通过整合全球和多尺度特征提取来改善红外和可见图像融合.
- 为了解决现有的基于变压器的融合方法的局限性,详细增强.
主要方法:
- 引入了一个超级特征注意力机制,用于远程依赖模型和全球特征提取.
- 采用波形导向聚合来提取多个规模的基本信息并增强细节.
- 利用简单的融合策略,通过网络强大的特征表示来实现.
主要成果:
- SFPFusion有效地提取全球和多尺度的特征,大大提高了细节的保存.
- 与最先进的方法相比,定性和定量实验显示出更高的性能.
- 拟议的方法在多个图像融合基准上取得了更好的融合结果.
结论:
- 通过优先考虑细节增强,SFPFusion为红外和可见图像融合提供了一个强大的解决方案.
- 超级特征注意力和波形引导聚合的集成提高了聚变网络的功能.
- 这种方法为未来研究细节感知图像融合提供了坚实的基础.
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