BVI-Lowlight: Fully Registered Benchmark Dataset for Low-Light Video
  Enhancement
Nantheera Anantrasirichai Ruirui Lin Alexandra Malyugina David Bull
Low-light videos often exhibit spatiotemporal incoherent noise, leading to
poor visibility and compromised performance across various computer vision
applications. One significant challenge in enhancing such content using modern
technologies is the scarcity of training data. This paper introduces a novel
low-light video dataset, consisting of 40 scenes captured in 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
subsequently, refine them via image-based post-processing to ensure the
pixel-wise alignment of frames in different light levels. This paper also
presents an exhaustive analysis of the low-light dataset, and demonstrates the
extensive and representative nature of our dataset in the context of supervised
learning. Our experimental results demonstrate the significance of fully
registered video pairs in the development of low-light video enhancement
methods and the need for comprehensive evaluation. Our dataset is available at
DOI:10.21227/mzny-8c77.