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.

翻译：许多基于Retinex理论的低光照图像增强（LLIE）方法忽略了该理论在数字成像中有效性的重要影响因素，如噪声、量化误差、非线性和动态范围溢出。本文通过对Retinex理论在数字成像中的理论与实验分析，提出了一种名为数字成像Retinex理论（DI-Retinex）的新表达式。该新表达式在增强模型中包含一个偏移项，从而允许通过非线性映射函数实现像素级亮度对比度调整。此外，为解决无监督条件下的低光照增强问题，我们在Gamma空间中提出了一种图像自适应掩蔽反向退化损失函数，并设计了一种用于调节附加偏移项的方差抑制损失函数。大量实验表明，所提方法在视觉效果、模型尺寸和处理速度方面均优于现有所有无监督方法。该算法还能辅助低光照环境下的下游人脸检测器，与其他方法相比在低光照增强后表现出最大的性能提升。