论文标题
弱光图像和视频增强：全面的调查及以后
Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
论文作者
论文摘要
本文介绍了对低光图像和视频增强的全面调查，解决了该领域的两个主要挑战。第一个挑战是混合过度/暴露不足图像的普遍性，现有方法无法充分解决。作为响应，这项工作介绍了SICE数据集的两个增强变体：SICE_GRAD和SICE_MIX，旨在更好地表示这些复杂性。第二个挑战是缺乏合适的低光视频数据集用于培训和测试。为了解决这个问题，该论文介绍了夜间Wenzhou数据集，Wenzhou数据集是一个大规模的高分辨率视频集合，具有挑战性的快速移动空中场景和街景，并具有不同的照明和退化。这项研究还对关键技术进行了广泛的分析，并使用建议的和当前的基准数据集进行了比较实验。该调查结束时，强调新兴应用程序，讨论尚未解决的挑战，并提出LLIE社区中未来的研究方向。这些数据集可在https://github.com/shenzheng2000/llie_survey上找到。
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.
