低光照图像增强研究方法综述  

Survey of Research Methods for Low Light Image Enhancement

作　　者：彭大鑫 甄彤 李智慧[1,2] PENG Daxin;ZHEN Tong;LI Zhihui(Key Laboratory of Grain Information Processing and Control of the Ministry of Education,Henan University of Technology,Zhengzhou 450001,China;College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学粮食信息处理与控制教育部重点实验室,郑州450001 [2]河南工业大学信息科学与工程学院,郑州450001

出　　处：《计算机工程与应用》2023年第18期14-27,共14页Computer Engineering and Applications

基　　金：国家重点研发计划项目(2017YFD0401004)。

摘　　要：低光照图像增强目的是从低光照条件下恢复细节完整的图像,并逐渐成为计算机图像处理研究的热点。图像成像的质量对于智能安防、视频监控等场景至关重要,且在相关行业中有着十分广阔的应用前景。为了深入研究低光照图像增强,对传统低光照图像增强方法进行详细地分类阐述与分析,列举了基于深度学习的图像增强方法,对所用到的各种网络以及所解决的问题进行了详细的梳理,并将所提到的方法进行了细致的对比。又对数据集进行了细致的分析和研究,并对一些常用的评价指标进行了简单梳理。对所述内容做出总结以及指出了当前研究中存在的困难,并指出了未来的研究目标。The purpose of low-light image enhancement is to restore images with complete details in low-light conditions,and it has gradually become a hot spot in computer image processing research.The quality of image imaging is crucial to intelligent security,video surveillance,and other scenarios and has a very broad application prospect in related industries.In order to study low-light image enhancement in depth,firstly,the traditional low-light image enhancement methods are classified and analyzed in detail,and then the image enhancement methods based on deep learning are listed,and the vari-ous networks used and the problems solved are detailed and compared the mentioned methods in detail.Then,the data set is analyzed and studied in detail,and some commonly used evaluation indicators are briefly sorted out.Finally,it summa-rizes the content,points out the difficulties in the current research,and points out the research goals for the future.

关 键 词：低光照图像增强 深度学习 RETINEX理论 低光照图像数据集 图像处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]