  Low-light videos often exhibit spatiotemporal incoherent noise, compromising
visibility and performance in computer vision applications. One significant
challenge in enhancing such content using deep learning is the scarcity of
training data. This paper introduces a novel low-light video dataset,
consisting of 40 scenes with various motion scenarios under two distinct
low-lighting conditions, incorporating genuine noise and temporal artifacts. We
provide fully registered ground truth data captured in normal light using a
programmable motorized dolly and refine it via an image-based approach for
pixel-wise frame alignment across different light levels. We provide benchmarks
based on four different technologies: convolutional neural networks,
transformers, diffusion models, and state space models (mamba). Our
experimental results demonstrate the significance of fully registered video
pairs for low-light video enhancement (LLVE) and the comprehensive evaluation
shows that the models trained with our dataset outperform those trained with
the existing datasets. Our dataset and links to benchmarks are publicly
available at https://doi.org/10.21227/mzny-8c77.
