实时语义驱动的红外和可见图像融合网络
Binhao Zheng1, Tieming Xiang1, Minghuang Lin1
1School of Electronic Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一个实时语义驱动的红外和可见图像融合框架 (RSDFusion). 这种新的方法通过保留重要的目标信息来增强融合,优于现有的方法.
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增强情境意识至关重要.
- 现有的核聚变方法往往难以保存关键目标细节.
研究的目的:
- 提出一个实时语义驱动的图像融合框架 (RSDFusion).
- 为了改善重要的信息的保留,特别是主要目标,在融合图像中.
主要方法:
- 利用预训练的语义细分模型来生成语义细分的图像.
- 从细分图像中提取目标面具,以指导特征提取.
- 设计了局部语义损失与结构相似性损失相结合的网络训练.
主要成果:
- 该RSDFusion框架表现出优越的性能比比较方法.
- 对公共数据集的主观和客观评估证实了它的有效性.
- 在融合输出中,从源图像中显著更好地保存主要目标.
结论:
- RSDFusion为红外和可见图像融合提供了有效的实时解决方案.
- 语义驱动的策略成功地提高了关键图像信息的保存.
- 这种方法推动了多模式图像融合领域的发展,以改善分析.
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