  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 unsolved open
issues. To examine the generalization of existing methods, we propose a
large-scale 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 the collected methods, datasets,
and evaluation metrics are publicly available and will be regularly updated at
https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open.
We will release our low-light image and video dataset.
