低光照图像增强算法综述  

The review of low-light image enhancement

作　　者：马龙 马腾宇 刘日升 Ma Long;Ma Tengyu;Liu Risheng(School of Software Technology,Dalian University of Technology,Dalian 116024,China;Pengcheng Laboratory,Shenzhen 510852,China;DUT-RU International School of Information Science and Engineering,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学软件学院,大连116024 [2]鹏程实验室,深圳510852 [3]大连理工大学—立命馆大学国际信息与软件学院,大连116024

出　　处：《中国图象图形学报》2022年第5期1392-1409,共18页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2020YFB1313503);国家自然科学基金项目(61922019);中央高校基本科研业务费专项资金资助。

摘　　要：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 lowlight 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

关 键 词：低光照图像增强 RETINEX理论 光照估计 深度学习 低光照人脸检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]