SALVE: Self-supervised Adaptive Low-light Video Enhancement
This work addresses low-light video enhancement for applications like surveillance or consumer video, but it appears incremental as it builds on existing retinex and regression techniques.
The paper tackled the problem of enhancing low-light videos by proposing SALVE, a self-supervised adaptive method that combines retinex-based image enhancement with ridge regression, resulting in 87% user preference over prior work.
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.