基于深度学习的低光图像增强的调查
Zhen Tian1,2, Peixin Qu1,2, Jielin Li1,2
1School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本文回顾了用于低光图像增强的深度学习方法,解决了亮度差和噪音等问题. 它涵盖了网络结构,数据集和评估指标,以提高图像质量.
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:
- 低亮度图像会受到亮度,对比度,色彩扭曲和噪音的降低.
- 有效的低光图像增强对于随后的图像处理任务至关重要.
研究的目的:
- 提供基于深度学习的低光图像增强的全面审查.
- 在这个领域系统地引入方法,数据集和评估指标.
主要方法:
- 审查各种深度学习网络结构,以在低光下进行增强.
- 描述低亮度图像质量评估方法.
- 组织和分析低光图像数据集.
主要成果:
- 对现有的深度学习方法的优缺点进行比较和分析.
- 确定关键方面,包括网络架构,培训数据和评估指标.
结论:
- 深度学习在低光图像增强方面提供了显著的进步.
- 概述了未来的研究方向,以进一步发展该领域.
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