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

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 4170 - 4174
• Main Authors: Zhang, Xu, Tang, Rui, Zhang, Guipeng, Kong, Dehui, Xu, Ke
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2024
• Subjects: Learning systems; light effects; Lighting; low-light image enhancement; PSNR; raw data; Signal processing; Signal processing algorithms; Speech enhancement; Training
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP48485.2024.10447333

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.