A self-supervised adaptive low-light video enhancement method, called SALVE, is proposed in this work. SALVE first enhances a few key frames of an input low-light video using a retinex-based low-light image enhancement technique. For each keyframe, it learns a mapping from low-light image patches to enhanced ones via ridge regression. These mappings are then used to enhance the remaining frames in the low-light video. The combination of traditional retinex-based image enhancement and learning-based ridge regression leads to a robust, adaptive and computationally inexpensive solution to enhance low-light videos. Our extensive experiments along with a user study show that 87% of participants prefer SALVE over prior work.

翻译：本文提出了一种名为SALVE的自监督自适应低光视频增强方法。该方法首先利用基于Retinex的低光图像增强技术对输入低光视频中的若干关键帧进行增强。针对每个关键帧，通过岭回归学习从低光图像块到增强图像块的映射关系。随后，这些映射被用于增强低光视频中的其余帧。将传统的基于Retinex的图像增强与基于学习的岭回归相结合，可得到一种鲁棒、自适应且计算成本低的低光视频增强解决方案。大量实验及用户研究表明，87%的参与者更倾向于选择SALVE而非先前的工作。