Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment

Low-light video enhancement is an important task. Previous work is mostly trained on paired static images or videos. We compile a new dataset formed by our new strategy that contains high-quality spatially-aligned video pairs from dynamic scenes in low- and normal-light conditions. We built it using a mechatronic system to precisely control the dynamics during the video capture process, and further align the video pairs, both spatially and temporally, by identifying the system's uniform motion stage. Besides the dataset, we propose an end-to-end framework, in which we design a self-supervised strategy to reduce noise, while enhancing the illumination based on the Retinex theory. Extensive experiments based on various metrics and large-scale user study demonstrate the value of our dataset and effectiveness of our method. The dataset and code are available at

PDF Abstract


Introduced in the Paper:

SDSD-indoor SDSD-outdoor

Used in the Paper:


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here