A self-supervised adaptive low-light video enhancement (SALVE) method is proposed in this work. SALVE first conducts an effective Retinex-based low-light image enhancement on a few key frames of an input low-light video. Next, it learns mappings from the low- to enhanced-light frames via Ridge regression. Finally, it uses these mappings to enhance the remaining frames in the input video. SALVE is a hybrid method that combines components from a traditional Retinex-based image enhancement method and a learning-based method. The former component leads to a robust solution which is easily adaptive to new real-world environments. The latter component offers a fast, computationally inexpensive and temporally consistent solution. We conduct extensive experiments to show the superior performance of SALVE. Our user study shows that 87% of participants prefer SALVE over prior work.
翻译：在这项工作中,提出了一种自监督的适应性低光视频增强(SALVE)方法。 SALVE首先在输入低光视频的几个关键框上,对基于Retinex的低光图像进行有效的增强。接下来,它通过Ridge回归从低光框到增强光框学习绘图。最后,它利用这些绘图来增强输入视频中的剩余框架。SALVE是一种混合方法,将基于传统Retinex的图像增强方法和基于学习的方法的组件结合起来。前一个组成部分导致一种对新的现实世界环境容易适应的稳健解决方案。后一个组成部分提供了一种快速、计算成本低和时间一致的解决方案。我们进行了广泛的实验,以展示SALVE的优异性表现。我们的用户研究表明,87%的参与者更喜欢SALVE而不是先前的工作。
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