GWRetinex-Net: Gray World Retinex Network for Low-Light Image Enhancement

In low-light environments, images often suffer from quality degradation issues such as low contrast, insufficient brightness, and color distortion due to inadequate light reaching the camera sensor. Most existing methods overlook the issue of color distortion caused by insufficient illumination when enhancing low-light images. In this work, we propose GWRetinex-Net, a novel deep learning network model based on Retinex theory and the gray world assumption. It consists of an image decomposition network, a reflection component enhancement network, and an illumination component enhancement network. During the training process of the image decomposition network, the gray world assumption is innovatively introduced to constrain the ill-posed decomposition problem caused by the absence of ground truth for reflection and illumination components, ensuring that the reflection components obtained after decomposition retain accurate color information. Based on the decomposition results, the reflection component enhancement network is responsible for mitigating degradation in the reflection components of low-light images, while the illumination component enhancement network focuses on adjusting the illumination distribution in the illumination components of low-light images. Comprehensive qualitative and quantitative experiments demonstrate that our GWRetinex-Net significantly outperforms comparative methods on multiple public datasets. Compared with the best-performing comparative methods, the images enhanced by the proposed method achieve an average improvement of 6.55% in SSIM and 8.87% in PSNR, along with an average decrease of 14.68% in MAE and 6.10% in NIQE. Additionally, object detection experiment in low-light environments further reveals the potential application value of GWRetinex-Net.