代网络用于基于共同特征的红外和可见光图像的差异预测
Ziang Zhang1, Li Li1, Weiqi Jin1
1MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究介绍了CFNet,这是一种使用红外和可见光图像进行准确深度预测的新型网络. CFNet有效地利用双带成像来改善异质图像对中的差异预测.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 多带成像应用正在扩大.
- 传统系统难以从异质图像中准确预测深度.
研究的目的:
- 调查CFNet,使用红外和可见光图像进行差异预测的代网络.
- 通过利用光谱互补性来提高深度预测的准确性.
主要方法:
- 开发了CFNet,具有共同的特征提取,上下文,多式联络信息获取和级联卷积门式反复子网络.
- 利用双带成像来考虑语义,几何和局部匹配的细节.
主要成果:
- 在公认的评估指标上,CFNet表现出卓越的表现.
- 视觉图像观察证实了CFNet与其他网络相比的有效性.
结论:
- CFNet提供了一种有效的技术方法,用于实际的异质图像差异预测.
- 该网络成功地利用双带成像进行准确的深度估计.
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