DI-Retinex: Digital-Imaging Retinex Model for Low-Light Image Enhancement

Many existing methods for low-light image enhancement (LLIE) based on Retinex model ignore important factors that affect the validity of this model 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 model (DI-Retinex) through theoretical and experimental analysis of Retinex model 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 low-light enhancement problem in an unsupervised manner, we propose an image-adaptive masked 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. We have released our code and model weights on https://github.com/sunshangquan/Di-Retinex.