DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image
  Enhancement
Shangquan Sun Wenqi Ren Jingyang Peng Fenglong Song Xiaochun Cao
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.