  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.

Computer Vision and Pattern Recognition;Image and Video Processing