FERFusion:用于红外和可见图像融合的快速有效的递归神经网络
Kaixuan Yang1,2,3,4, Wei Xiang1,2, Zhenshuai Chen1,2,3,4
1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究介绍了一种快速,高效的递归神经网络,用于红外和可见图像融合. 该模型在训练过程中以最小的参数和减少的计算资源实现了出色的融合结果.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 深度神经网络广泛用于红外和可见图像融合.
- 现有的核聚变模型经常受到高参数计数和大量资源消耗的影响.
研究的目的:
- 提出一个快速高效的递归融合神经网络模型.
- 在计算和空间资源方面解决现有模型的局限性.
主要方法:
- 设计了一个注意力模块,将传统的融合知识与道注意力相结合,以便有效地提取特征.
- 利用共享的关注层来早期融合模式共享的功能.
- 采用并行扩展的卷积层来最大限度地减少网络参数.
主要成果:
- 递归训练方法导致最小的模型参数和优秀的融合结果,训练批次很少.
- 在模型训练过程中显著减少了时间,空间和计算资源.
- 在培训效率和融合质量方面,在三个公共数据集上表现优于九种最先进的 (SOTA) 方法.
结论:
- 拟议的递归融合神经网络为红外和可见图像融合提供了有效的解决方案.
- 该模型实现了高质量的融合结果,同时要求更少的计算资源.
- 这种方法适用于资源有限的环境,并加快了培训过程.
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