低光照图像增强旨在提高光照不足场景下捕获数据的视觉感知质量以获取更多信息，逐渐成为图像处理领域中的研究热点，在自动驾驶、安防等人工智能相关行业中具有十分广阔的应用前景。传统的低光照图像增强技术往往需要高深的数学技巧以及严格的数学推导，且导出的迭代过程普遍流程复杂，不利于实际应用。随着大规模数据集的相继诞生，基于深度学习的低光照图像增强已经成为当前的主流技术，然而此类技术受限于数据分布，存在性能不稳定、应用场景单一等问题。此外，在低光照环境下的高层视觉任务(如目标检测)对于低光照图像增强技术的发展带来了新的机遇与挑战。本文从3个方面系统地综述了低光照图像增强技术的研究现状。介绍了现有低光照图像数据集，详述了低光照图像增强技术的发展脉络，通过对比低光照图像增强质量与夜间人脸检测精度，进一步对现有低光照增强技术进行了全面评估与分析。基于对上述现状的探讨，结合实际应用，本文指出当前技术的局限性，并对其发展趋势进行预测。

Low-light image enhancement aims to improve the visual perception quality of captured data in the context of low-light scenarios. The purpose of low-light image enhancement is to improve the visual quality via image brightness enhancement. Low-light image enhancement is a key factor to low-light face detection and nighttime semantic segmentation.Our systematic and detailed review is focused on the recent development of low-light image enhancement., We first carry out a comprehensive and systematic analysis for low-light image enhancement on the three aspects as mentioned below:1) the development of low-light image datasets, 2) the development of low-light image enhancement technology, and 3) the experimental evaluation synthesis. Finally, our demonstrated results are summarized and forecasted in related to low-light image enhancement further.First of all, as far as the existing low-light image enhancement data set is concerned, it reveals a trend in the scale of sizes (small to large), multi-scenarios(solo to diverse), and data involvement degree(simple to complex). Most of the data sets are attributed to unpaired data, and the target pairwise data sets cannot be effectively synthesized due to the difficulty of illumination in low-light image enhancement modeling. The existing pairs of low illumination image data set labels are mainly subjected to manual parameter settings like the exposure time adjustment or expertise modification).The existing reference images in pairs of data sets have challenged to represent the scene information captured in low-light observation accurately. In addition, the construction process of some data sets is relevant to detection or segmentation labels. It is necessary to establish a connection and explore the impact of low-level visual tasks with high-level visual tasks and faciliate high-level visual tasks like detection and semantic segmentation in a low-light scenario.Second, existing low-light image enhancement techniques can be roughly divided into three categories:1) distribution-based mapping, 2)model-based optimization, and 3) deep learning, respectively. Data-driven deep learning technology has significantly promoted the development of low-light image enhancement. Thanks to the development of the existing low-light image enhancement technology, the traditional model design method has been transformed into data-driven deep learning technology. Among them, It can resolve low-light image enhancement issue based on mapping the value distribution of low-light input to amplify smaller values (displayed as dark), while exposure unevenness is still the a challenged issue to deal with. The model optimization based methods make assumptions about the ground truth via a priori regular condition designation, and do not depend on the amount of training data, but achieve relatively stable performance through the image analysis itself. The deep learning based image enhancement method is to learn via a large amount of training data and realize image enhancement based on designing a deep network-related/independent of the physical model. The trend moves towards semi-supervised/unsupervised/self-supervised learning mechanisms from fully supervised learning mechanisms and focuses more on image enhancement quality when pairwise ground truth data is not available. However, the loss function design in the training process and the training relies on the design of the loss function and the adjustment of network parameters. The existing enhanced network structure is gradually changing from complex to lightweight. Simultaneously, the improvement of visual quality is related to the running speed of the network. There is a lack of effective indicators that can accurately reflect the enhanced image quality due to the specialty of the low-light image enhancement. Currently, a series of downstream high-level vision tasks have been adopting to evaluate the quality of enhanced images and transferring to user-friendly low-light visual image enhancement to high-level vision task performance priority.Meanwhile, a series of experimental evaluations demonstrate that existing optimized model based methods have better generalization ability than those deep learning based methods, while existing unsupervised learning techniques are more robust and efficient than fully supervised learning methods. It can be obtained from the high-level vision tasks in low-light scenes that low-light enhancement has a certain effect on more tasks although good visual effects has not obtained higher high-level vision task accuracy. This indirect confirmation of the disparity of the visual quality expression is orientated from existing works and high-level visual tasks. It is worth noting that the results obtained of trained networks on paired data lack generality and make it difficult to characterize natural image distributions. The unsupervised method can generate enhanced results to satisfy the natural image distribution via ta natural image distribution related loss function, The following four potential research perspectives are proposed, including 1)the inherent laws issue of low-light images in different scenes and reduce the dependence on paired data to endow the algorithm with scene-independent generalization ability; 2) an efficient network framework construction for low-light image enhancement tasks; 3) an effective learning strategy to make the framework learn completely; and 4) a connection between low-light image enhancement and high-level vision tasks (e.g., detection).