论文标题
salve：自我监督的自适应低光视频增强
SALVE: Self-supervised Adaptive Low-light Video Enhancement
论文作者
论文摘要
在这项工作中提出了一种称为salve的自我监督的自适应低光视频增强方法。 Salve首先使用基于Etinex的低光图像增强技术增强了输入低光视频的几个关键帧。对于每个密钥帧，它通过山脊回归学习了从弱光图像贴片到增强图像的映射。然后，这些映射用于增强弱光视频中的剩余帧。传统的基于Itinex的图像增强和基于学习的脊回归的结合可提供强大，适应性和计算廉价的解决方案，以增强低光视频。我们的广泛实验以及用户研究表明，有87％的参与者比先前的工作更喜欢分解。
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.
