Low-Light Raw Image Enhancement on a Dataset Suffering Light Effects

Abstract

Deep learning-based methods have achieved remarkable success in low-light image enhancement (LLIE). But most existing works are based on sRGB data and do not focus on the light effects in bright regions when enhancing low-light regions. This inevitably leads to excessive enhancement and saturation of bright regions, resulting in reduced contrast and inaccurate color. To address this problem, a low-light raw dataset covering diverse lighting conditions is proposed to overcome the limitations of the existing datasets and to supervise the training of our model. Then, we design a new enhancement network that incorporates global information to learn mapping curves from low-light images to Ground Truth (GT). A novel loss function is also proposed to help achieve high-quality enhancement for a low-light raw image suffering light effects. In terms of qualitative evaluations, our approach performs best in suppressing light effects and boosting the intensity of dark regions compared with other state-of-the-art low-light algorithms. In quantitative tests, it is also shown that the proposed method has the highest peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), suggesting a superior enhancement performance.