  This paper presents a comprehensive survey of low-light image and video
enhancement, addressing two primary challenges in the field. The first
challenge is the prevalence of mixed over-/under-exposed images, which are not
adequately addressed by existing methods. In response, this work introduces two
enhanced variants of the SICE dataset: SICE_Grad and SICE_Mix, designed to
better represent these complexities. The second challenge is the scarcity of
suitable low-light video datasets for training and testing. To address this,
the paper introduces the Night Wenzhou dataset, a large-scale, high-resolution
video collection that features challenging fast-moving aerial scenes and
streetscapes with varied illuminations and degradation. This study also
conducts an extensive analysis of key techniques and performs comparative
experiments using the proposed and current benchmark datasets. The survey
concludes by highlighting emerging applications, discussing unresolved
challenges, and suggesting future research directions within the LLIE
community. The datasets are available at
https://github.com/ShenZheng2000/LLIE_Survey.
