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一种零拍摄低光图像增强方法,集成门机制

Junhao Tian¹, Jianwei Zhang¹

¹School of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|August 26, 2023

概括

这项研究引入了一种新的零参考深度学习网络,用于低光图像增强. 它有效地提高了没有配对数据的图像质量,在各种设备上展示了实际可行性.

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:

恶劣的环境照明会导致像低亮度和噪音等图像退化.
深度学习方法存在,但需要配对的训练数据,这往往是不可用的.
现有的方法在获取配对训练数据以进行低光图像增强方面面临着挑战.

研究的目的:

为低光条件提出一个零参考图像增强网络.
解决现有的深度学习方法中的配对培训数据的局限性.
开发一种无监督的方法来改善未曝光图像中的照明.

主要方法:

使用改进的编码解码器结构用于特征提取和参数矩阵生成.
使用参数矩阵构建增强曲线,用于代图像增强.
采用四个非参考损失函数用于参数估计网络的无监督训练.

主要成果:

在NIQE,PIQE和BRISQUE非参考评价指数上取得了比现有方法更好的表现.
废弃实验证实了拟议网络中关键组件的有效性.
在PC和移动设备上证明了实际可行性和性能.

结论:

拟议的零参考网络有效地增强了没有配对数据的低光图像.
使用非参考损失函数的无监督方法被证明是稳健和高效的.
该方法适用于实际应用,在各种设备上提供更好的图像质量和性能.

关键词:

轻量级的轻量级的轻量级的轻量级的在低光下增强低光增强.变压器变压器变压器变压器没有射击的零射击.

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