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半DDM-天气: 一个半监督的学习框架,用于全面消除恶劣天气
Fang Long1, Wenkang Su1, Zixuan Li2
1School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, Guangdong, China.
概括
本研究介绍了SemiDDM-Weather,这是一个新的半监督框架,用于消除不利的天气影响. 它有效地清除各种天气条件使用有限的标记数据,实现卓越的视觉质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 恶劣的天气条件显著降低了图像和视频的视觉质量.
- 当前的天气移除技术往往需要广泛的标记数据,并且特定于某些天气类型.
- 开发一个多功能和数据效率高的解决方案,以实现全合一的恶劣天气移除至关重要.
研究的目的:
- 为了呈现一个开创性的半监督框架,为所有在一个不利的天气移除.
- 克服现有方法数据依赖性和天气类型特异性的局限性.
- 通过使用有限的标记数据,提高恶劣天气移除的效率和有效性.
主要方法:
- 一个教师-学生网络架构被采用一个Denoising扩散模型 (DDM) 作为骨干 (SemiDDM-Weather).
- 波形扩散模型 (波形扩散) 适应了定制的输入和损失函数,以实现高效的多对一映射.
- 引入了质量评估和内容一致性约束,以完善强大的半监督学习的伪标签.
主要成果:
- 半DDM-Weather显示了高视觉质量和优异的性能,在合成和现实世界的数据集中消除恶劣的天气.
- 该框架有效地处理各种天气条件,优于完全监督的方法.
- 半监督方法显著减少了对广泛标记数据的需求.
结论:
- 半DDM-Weather提供了一种有效和高效的解决方案,可以全面消除恶劣天气.
- 提出的质量评估和内容一致性机制提高了半监督学习的稳定性.
- 该框架通过在不利条件下实现高质量的图像恢复,减少数据需求,从而推动该领域的发展.
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