This paper presents a comprehensive survey of low-light image and video
enhancement. We begin with the challenging mixed over-/under-exposed images,
which are under-performed by existing methods. To this end, we propose two
variants of the SICE dataset named SICE_Grad and SICE_Mix. Next, we introduce
Night Wenzhou, a large-scale, high-resolution video dataset, to address the
issue of the lack of a low-light video dataset that discount the use of
low-light image enhancement (LLIE) to videos. Our Night Wenzhou dataset is
challenging since it consists of fast-moving aerial scenes and streetscapes
with varying illuminations and degradation. We conduct extensive key technique
analysis and experimental comparisons for representative LLIE approaches using
these newly proposed datasets and the current benchmark datasets. Finally, we
address unresolved issues and propose future research topics for the LLIE
community. Our datasets are available at
https://github.com/ShenZheng2000/LLIE_Survey.Comment: 13 pages, 8 tables, and 13 figure