In this paper, we present a systematic review and evaluation of existing single-image low-light enhancement algorithms. Besides the commonly used low-level vision oriented evaluations, we additionally consider measuring machine vision performance in the low-light condition via face detection task to explore the potential of joint optimization of high-level and low-level vision enhancement. To this end, we first propose a large-scale low-light image dataset serving both low/high-level vision with diversified scenes and contents as well as complex degradation in real scenarios, called Vision Enhancement in the LOw-Light condition (VE-LOL). Beyond paired low/normal-light images without annotations, we additionally include the analysis resource related to human, i.e. face images in the low-light condition with annotated face bounding boxes. Then, efforts are made on benchmarking from the perspective of both human and machine visions. A rich variety of criteria is used for the low-level vision evaluation, including full-reference, no-reference, and semantic similarity metrics. We also measure the effects of the low-light enhancement on face detection in the low-light condition. State-of-the-art face detection methods are used in the evaluation. Furthermore, with the rich material of VE-LOL, we explore the novel problem of joint low-light enhancement and face detection. We develop an enhanced face detector to apply low-light enhancement and face detection jointly. The features extracted by the enhancement module are fed to the successive layer with the same resolution of the detection module. Thus, these features are intertwined together to unitedly learn useful information across two phases, i.e. enhancement and detection. Experiments on VE-LOL provide a comparison of state-of-the-art low-light enhancement algorithms, point out their limitations, and suggest promising future directions. Our dataset has supported the Track “Face Detection in Low Light Conditions” of CVPR UG2+ Challenge (2019–2020) (http://cvpr2020.ug2challenge.org/).

在本文中，我们对现有的单图像弱光增强算法进行了系统的回顾和评估。除了常用的面向低级视觉的评估外，我们还考虑通过人脸检测任务在低光照条件下测量机器视觉性能，以探索联合优化高级和低级视觉增强的潜力。为此，我们首先提出了一个大规模的低照度图像数据集，可同时满足低/高视力，场景和内容的多样化以及真实场景中的复杂退化，称为低光条件下的视觉增强（VE-大声笑）。除了没有注释的成对的低光/正常光图像之外，我们还包括与人有关的分析资源，即在低光条件下带有带注释的面部边界框的面部图像。然后，从人和机器视觉的角度进行基准测试。低级视觉评估使用了多种标准，包括完全参考，无参考和语义相似性度量。我们还测量了弱光增强对弱光条件下人脸检测的影响。评估中使用了最新的面部检测方法。此外，借助丰富的VE-LOL材料，我们探索了弱光联合和面部检测的新问题。我们开发了一种增强型人脸检测器，以结合应用弱光增强和人脸检测。由增强模块提取的特征以与检测模块相同的分辨率被馈送到连续层。从而，这些功能相互交织在一起，可以跨两个阶段（即增强和检测）统一学习有用的信息。VE-LOL上的实验提供了最新的微光增强算法的比较，指出了它们的局限性，并提出了有希望的未来方向。我们的数据集支持CVPR UG2 +挑战赛（2019–2020）（http://cvpr2020.ug2challenge.org/）的“弱光条件下的面部检测”追踪。