通过低照明恢复图像到正常照明网络,基于Retinex理论
Chaoran Wen1,2, Ting Nie1, Mingxuan Li1
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了一种有效的基于Retinex的网络,用于增强低照明图像. 拟议的方法通过减少噪音和增强细节,显著提高图像质量,优于现有技术.
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:
- 低照明条件严重降低图像质量,引入噪音,工件和降低亮度.
- 在依赖视觉数据的各种应用中,有效的低光图像增强是至关重要的.
研究的目的:
- 基于Retinex理论,提出一个有效的低照明图像增强网络.
- 为了解决视觉问题,如噪音,文物和低光图像中的亮度降低.
主要方法:
- 图像通过分解网络被分解成反射率和照明组件.
- 一个具有全球和本地残余卷积块的重建网络否定了反射功率组件.
- 增强网络利用频率信息,注意力机制,以及用于照明元件的残余密度网络.
主要成果:
- 与现有方法相比,拟议的方法在量化指标方面表现优越.
- 视觉评估证实了拟议方法在增强低光图像方面的有效性.
- 在公共数据集上进行的实验验证实了该方法的稳定性和有效性.
结论:
- 开发的基于Retinex的网络有效地增强了低照明图像.
- 该方法成功地减轻了噪音和工件,同时提高了亮度和对比度.
- 这种方法在低光图像增强技术方面取得了重大进展.
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