  Low-light image enhancement (LLIE) aims at improving the perception or
interpretability of an image captured in an environment with poor illumination.
Recent advances in this area are dominated by deep learning-based solutions,
where many learning strategies, network structures, loss functions, training
data, etc. have been employed. In this paper, we provide a comprehensive survey
to cover various aspects ranging from algorithm taxonomy to open issues. To
examine the generalization of existing methods, we propose a low-light image
and video dataset, in which the images and videos are taken by different mobile
phones' cameras under diverse illumination conditions. Besides, for the first
time, we provide a unified online platform that covers many popular LLIE
methods, of which the results can be produced through a user-friendly web
interface. In addition to qualitative and quantitative evaluation of existing
methods on publicly available and our proposed datasets, we also validate their
performance in face detection in the dark.This survey together with the
proposed dataset and online platform could serve as a reference source for
future study and promote the development of this research field. The proposed
platform and dataset as well as the collected methods, datasets, and evaluation
metrics are publicly available and will be regularly updated.
