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 lack of low-light video datasets that discourages the use of low-light image enhancement (LLIE) methods in videos. Our Night Wenzhou dataset is challenging since it consists of fast-moving aerial scenes and streetscapes with varying illuminations and degradation. We then construct a hierarchical taxonomy, conduct extensive key technique analysis, and performs experimental comparisons for representative LLIE approaches using our proposed datasets and the current benchmark datasets. Finally, we identify emerging applications, address unresolved challenges, and propose future research topics for the LLIE community. Our 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进行了全面综述。首先，我们聚焦于具有挑战性的混合过曝/欠曝图像，现有方法对此类图像表现欠佳。为此，我们提出了SICE数据集的两个变体，即SICE_Grad与SICE_Mix。其次，我们引入大规模高分辨率视频数据集Night Wenzhou，以解决低光照视频数据集匮乏导致低光照图像增强方法难以应用于视频的问题。Night Wenzhou数据集包含快速移动的航拍场景与光照变化及退化程度各异的街景，具有高度挑战性。随后，我们构建了层次化分类体系，开展了广泛的关键技术分析，并利用所提数据集及现有基准数据集对代表性低光照图像增强方法进行了实验对比。最后，我们识别了新兴应用场景，指出了未解决的技术难点，并为低光照图像增强领域提出了未来研究方向。我们的数据集已公开于https://github.com/ShenZheng2000/LLIE_Survey。