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.

翻译：本文对低光照图像与视频增强技术进行了全面综述，重点聚焦该领域的两大核心挑战。其一，现有方法难以有效应对普遍存在的混合过曝/欠曝图像。为此，本研究提出SICE数据集的两种增强变体——SICE_Grad与SICE_Mix，旨在更精准地反映此类复杂场景特性。其二，现有低光照视频数据集的匮乏制约了模型训练与测试。针对此问题，论文引入"夜温州"（Night Wenzhou）数据集，该大规模高分辨率视频集合涵盖快速移动的空中场景及具有多样光照与退化特征的城市街景。本研究同时深入剖析关键技术环节，并基于所提数据集与现有基准数据集开展对比实验。综述最后总结了新兴应用场景、待解决的挑战性问题，并为低光照图像增强（LLIE）领域指明未来研究方向。相关数据集已在https://github.com/ShenZheng2000/LLIE_Survey 开源。