Many existing methods for low-light image enhancement (LLIE) based on Retinex
theory ignore important factors that affect the validity of this theory in
digital imaging, such as noise, quantization error, non-linearity, and dynamic
range overflow. In this paper, we propose a new expression called
Digital-Imaging Retinex theory (DI-Retinex) through theoretical and
experimental analysis of Retinex theory in digital imaging. Our new expression
includes an offset term in the enhancement model, which allows for pixel-wise
brightness contrast adjustment with a non-linear mapping function. In addition,
to solve the lowlight enhancement problem in an unsupervised manner, we propose
an image-adaptive masked reverse degradation loss in Gamma space. We also
design a variance suppression loss for regulating the additional offset term.
Extensive experiments show that our proposed method outperforms all existing
unsupervised methods in terms of visual quality, model size, and speed. Our
algorithm can also assist downstream face detectors in low-light, as it shows
the most performance gain after the low-light enhancement compared to other
methods.

通过对数字成像中的 Retinex 理论进行理论和实验分析，本文提出了一种名为数字成像 Retinex 理论（DI-Retinex）的新表达式，该表达式包括增强模型中的偏移项，允许使用非线性映射函数进行像素级亮度对比度调整，并通过 Gamma 空间提出了一种图像自适应掩蔽逆恢复损失来解决低光增强问题，同时设计了一种方差抑制损失来调节附加偏移项，实验结果表明我们的方法在视觉质量、模型大小和速度等方面优于所有现有的无监督方法，还可以在低光条件下辅助人脸检测器，与其他方法相比，在低光增强后面部检测的性能增益最显著。