计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 123-133.doi: 10.11896/jsjkx.211000007

• 计算机视觉:理论与应用 • 上一篇    下一篇

基于深度学习的视频超分辨率重构进展综述

冷佳旭1,2, 王佳1, 莫梦竟成1, 陈泰岳1, 高新波1   

  1. 1 重庆邮电大学图像认知重庆市重点实验室 重庆400065
    2 南京理工大学江苏省社会安全图像与视频理解重点实验室 南京210094
  • 收稿日期:2021-09-30 修回日期:2021-11-04 出版日期:2022-02-15 发布日期:2022-02-23
  • 通讯作者: 高新波(gaoxb@cqupt.edu.cn)
  • 作者简介:lengjx@cqupt.edu.cn
  • 基金资助:
    国家自然科学基金(62036007,62050175,62102057);重庆市教委科学技术研究项目(KJQN-202100627)

Survey on Video Super-resolution Based on Deep Learning

LENG Jia-xu1,2, WANG Jia1, MO Meng-jing-cheng1, CHEN Tai-yue1, GAO Xin-bo1   

  1. 1 Key Laboratory of Image Cognition,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    2 Jiangsu Key Laboratory of Image and Video Understanding for Social Safety,Nanjing University of Science and Technology,Nanjing 210094,China
  • Received:2021-09-30 Revised:2021-11-04 Online:2022-02-15 Published:2022-02-23
  • About author:LENG Jia-xu,born in 1989,Ph.D.His main research interests include object detection,face super-resolution,person re-identification and video anomaly detection.
    GAO Xin-bo,born in 1972,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,machine learning,computer vision and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(62036007,62050175,62102057) and Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN-202100627).

摘要: 视频超分辨率是根据给定的低分辨率视频序列恢复其对应的高分辨率视频帧的过程。近年来,VSR在深度学习的驱动下取得了重大突破。为了进一步促进VSR的发展,文中对基于深度学习的VSR算法进行了归类、分析和比较。首先,根据网络结构将现有方法分为两大类,即基于迭代网络的VSR和基于递归网络的VSR,并对比分析了不同网络模型的优缺点。然后,全面介绍了VSR数据集,并在一些常用的公共数据集上对已有算法进行了总结和比较。最后,对VSR算法中的关键问题进行了分析,并对其应用前景进行了展望。

关键词: 视频超分辨率, 深度学习, 卷积神经网络, 帧间信息

Abstract: Video super-resolution (VSR) aims to reconstruct a high-resolution video from its corresponding low-resolution version.Recently,VSR has made great progress driven by deep learning.In order to further promote VSR,this survey makes a comprehensive summary of VSR,and makes a taxonomy,analysis and comparison of existing algorithms.Firstly,since different frameworks are very important for VSR,we group the VSR approaches into two categories according to different frameworks:iterative- and recurrent-network based VSR approaches.The advantages and disadvantages of different networks are further compared and analyzed.Secondly,we comprehensively introduce the VSR datasets,summarize existing algorithms and further compare these algorithms on some benchmark datasets.Finally,the key challenges and the application of VSR methods are analyzed and prospected.

Key words: Video super-resolution, Deep learning, Convolutional neural network, Inter-frame information

中图分类号: 

  • TP183
[1]HARRIS J L.Diffraction and resolving power[J].JOSA,1964,54(7):931-936.
[2]CAPEL D,ZISSERMAN A.Super-resolution enhancement oftext image sequences[C]//Proceedings 15th International Conference on Pattern Recognition.2000:600-605.
[3]SCHULTZ R R,STEVENSON R L.Extraction of high-resolution frames from video sequences[J].IEEE Transactions on Image Processing,1996,5(6):996-1011.
[4]BORMAN S,STEVENSON R L.Simultaneous multi-frameMAP super-resolution video enhancement using spatio-temporal priors[C]//Proceedings 1999 International Conference on Image Processing (Cat.99CH36348).1999:469-473.
[5]GUNTURK B K,ALTUNBASAK Y,MERSEREAU R.Baye-sian resolution-enhancement framework for transform-coded video[C]//Proceedings 2001 International Conference on Image Processing (Cat.No.01CH37205).2001:41-44.
[6]PATTI A J,SEZAN M I,TEKALP A M.Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time[J].IEEE Transactions on Image Processing,1997,6(8):1064-1076.
[7]KAPPELER A,YOO S,DAI Q,et al.Video super-resolutionwith convolutional neural networks[J].IEEE Transactions on Computational Imaging,2016,2(2):109-122.
[8]XUE T,CHEN B,WU J,et al.Video enhancement with task-oriented flow[J].International Journal of Computer Vision,2019,127(8):1106-1125.
[9]HARIS M,SHAKHNAROVICH G,UKITA N.Recurrentback-projection network for video super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:3897-3906.
[10]LI F,BAI H,ZHAO Y.Learning a deep dual attention network for video super-resolution[J].IEEE Transactions on Image Processing,2020,29:4474-4488.
[11]WANG Z,CHEN J,HOI S C H.Deep learning for image super-resolution:A survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020:3365-3387.
[12]SINGH A,SINGH J.Survey on single image based super-resolution—implementation challenges and solutions[J].Multimedia Tools and Applications,2020,79(3):1641-1672.
[13]YANG W,ZHANG X,TIAN Y,et al.Deep learning for single image super-resolution:A brief review[J].IEEE Transactions on Multimedia,2019,21(12):3106-3121.
[14]DAITHANKAR M V,RUIKAR S D.Video Super Resolution:A Review[C]//ICDSMLA.2020:488-495.
[15]WU Y,FAN G H.Survey of Super-Resolution ReconstructionTechniques for Video Sequences[J].Computer Engineering & Software,2017,38(4):154-160.
[16]LIU H,RUAN Z,ZHAO P,et al.Video super resolution based on deep learning:A comprehensive survey[J].arXiv:2007.12928,2020.
[17]DONG C,LOY C C,HE K,et al.Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(2):295-307.
[18]DONG C,LOY C C,TANG X.Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision.2016:391-407.
[19]KIM J,LEE J K,LEE K M.Accurate image super-resolutionusing very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1646-1654.
[20]SHI W,CABALLERO J,HUSZAR F,et al.Real-time singleimage and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1874-1883.
[21]ZHANG Y,LI K,LI K,et al.Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:286-301.
[22]BAO W,LAI W S,ZHANG X,et al.Memc-net:Motion estimation and motion compensation driven neural network for video interpolation and enhancement[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019:933-948.
[23]JO Y,OH S W,KANG J,et al.Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3224-3232.
[24]TIAN Y,ZHANG Y,FU Y,et al.Tdan:Temporally-deformablealignment network for video super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:3360-3369.
[25]CABALLERO J,LEDIG C,AITKEN A,et al.Real-time videosuper-resolution with spatio-temporal networks and motion compensation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4778-4787.
[26]GUO J,CHAO H.Building an end-to-end spatial-temporal con-volutional network for video super-resolution[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017:4053-4060.
[27]YI P,WANG Z,JIANG K,et al.Omniscient Video Super-Resolution[J].arXiv:2103.15683,2021.
[28]CHAN K C K,WANG X,YU K,et al.BasicVSR:The searchfor essential components in video super-resolution and beyond[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:4947-4956.
[29]LUCAS B.An Iterative Image Registration Technique with an Application to Stereo Vision (DARPA)[J].Proc. IJCAI,1981,81(3):674-679.
[30]DRULEA M,NEDEVSCHI S.Total variation regularization of local-global optical flow[C]//2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).2011:318-323.
[31]DOSOVITSKIY A,FISCHER P,ILG E,et al.Flownet:Lear-ning optical flow with convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:2758-2766.
[32]ILG E,MAYER N,SAIKIA T,et al.Flownet 2.0:Evolution of optical flow estimation with deep networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2462-2470.
[33]RANJAN A,BLACK M J.Optical flow estimation using a spatial pyramid network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4161-4170.
[34]TAO X,GAO H,LIAO R,et al.Detail-revealing deep video super-resolution[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:4472-4480.
[35]WANG L,GUO Y,LIN Z,et al.Learning for video super-resolution through HR optical flow estimation[C]//Asian Confe-rence on Computer Vision.2018:514-529.
[36]BARE B,YAN B,MA C,et al.Real-time video super-resolution via motion convolution kernel estimation[J].Neurocomputing,2019,367:236-245.
[37]WANG Z,YI P,JIANG K,et al.Multi-memory convolutionalneural network for video super-resolution[J].IEEE Transactions on Image Processing,2018,28(5):2530-2544.
[38]KALAROT R,PORIKLI F.Multiboot vsr:Multi-stage multi-reference bootstrapping for video super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:2060-2069.
[39]SHI X J,CHEN Z,WANG H,et al.Convolutional LSTM network:A machine learning approach for precipitation nowcasting[C]//Advances in Neural Information Processing Systems.2015:802-810.
[40]IRANI M,PELEG S.Improving resolution by image registration[J].CVGIP:Graphical Models and Image Processing,1991,53(3):231-239.
[41]LIU D,WANG Z,FAN Y,et al.Robust video super-resolution with learned temporal dynamics[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2507-2515.
[42]KIM T H,SAJJADI M S M,HIRSCH M,et al.Spatio-temporal transformer network for video restoration[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:106-122.
[43]LIU H,ZHAO P,RUAN Z,et al.Large motion video super-re-solution with dual subnet and multi-stage communicated upsampling[J].arXiv:2103.11744,2021.
[44]DAI J,QI H,XIONG Y,et al.Deformable convolutional net-works[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:764-773.
[45]ZHU X,HU H,LIN S,et al.Deformable convnets v2:More deformable,better results[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:9308-9316.
[46]WANG X,CHAN K C K,YU K,et al.Edvr:Video restoration with enhanced deformable convolutional networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:1954-1963.
[47]WANG H,SU D,LIU C,et al.Deformable non-local networkfor video super-resolution[J].IEEE Access,2019,7:177734-177744.
[48]SONG H,XU W,LIU D,et al.Multi-Stage Feature Fusion Network for Video Super-Resolution[J].IEEE Transactions on Image Processing,2021,30:2923-2934.
[49]LUCAS A,LOPEZ-TAPIA S,MOLINA R,et al.Generative adversarial networks and perceptual losses for video super-resolution[J].IEEE Transactions on Image Processing,2019,28(7):3312-3327.
[50]YI P,WANG Z,JIANG K,et al.Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:3106-3115.
[51]ISOBE T,LI S,JIA X,et al.Video super-resolution with temporal group attention[C]//Proceedings of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2020:8008-8017.
[52]LI W,TAO X,GUO T,et al.Mucan:Multi-correspondence aggregation network for video super-resolution[C]//European Conference on Computer Vision.2020:335-351.
[53]SAJJADI M S M,VEMULAPALLI R,BROWN M.Frame-recurrent video super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6626-6634.
[54]YIN B,LIN C,TAN W.Frame and feature-context video super-resolution[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019,33(1):5597-5604.
[55]ZHU X,LI Z,ZHANG X Y,et al.Residual invertible spatio-temporal network for video super-resolution[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:5981-5988.
[56]JACOBSEN J H,SMEULDERS A,OYALLON E.i-revnet:Deep invertible networks[J].arXiv:1802.07088,2018.
[57]ISOBE T,JIA X,GU S,et al.Video super-resolution with recurrent structure-detail network[C]//European Conference on Computer Vision.2020:645-660.
[58]HUANG Y,WANG W,WANG L.Bidirectional recurrent con-volutional networks for multi-frame super-resolution[J].Advances in Neural Information Processing Systems,2015,28:235-243.
[59]GUO J,CHAO H.Building an end-to-end spatial-temporal con-volutional network for video super-resolution[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017:4053-4060.
[60]LI D,LIU Y,WANG Z.Video super-resolution using non-simultaneous fully recurrent convolutional network[J].IEEE Tran-sactions on Image Processing,2018,28(3):1342-1355.
[61]FUOLI D,GU S,TIMOFTE R.Efficient video super-resolution through recurrent latent space propagation[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).IEEE,2019:3476-3485.
[62]LIAO R,TAO X,LI R,et al.Video super-resolution via deep draft-ensemble learning[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:531-539.
[63]NAH S,BAIK S,HONG S,et al.Ntire 2019 challenge on video deblurring and super-resolution:Dataset and study[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:1996-2005.
[64]LIU C,SUN D.On Bayesian Adaptive Video Super Resolution[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,36(2):346-360.
[65]ZENG H,ZHANG X,YU Z,et al.SR-ITM-GAN:Learning 4K UHD HDR With a Generative Adversarial Network[J].IEEE Access,2020,8:182815-182827.
[66]HE Z,HUANG H,JIANG M,et al.FPGA-based real-time super-resolution system for ultra high definition videos[C]//2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).IEEE,2018:181-188.
[67]LIU Z,CUI C.A New Low Bit-Rate Coding Scheme for Ultra High Definition Video Based on Super-Resolution Reconstruction[C]//2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET).IEEE,2018:325-329.
[68]KIM Y,CHOI J S,KIM M.A real-time convolutional neuralnetwork for super-resolution on FPGA with applications to 4K UHD 60 fps video services[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,29(8):2521-2534.
[69]YANG Y,BI P,LIU Y.License plate image super-resolutionbased on convolutional neural network[C]//2018 IEEE 3rd International Conference on Image,Vision and Computing (ICIVC).IEEE,2018:723-727.
[70]GHONEIM M,REHAN M,OTHMAN H.Using super resolution to enhance license plates recognition accuracy[C]//2017 12th International Conference on Computer Engineering and Systems (ICCES).2017:515-518.
[71]MEHREGAN K,AHMADYFARD A,KHOSRAVI H.Super-resolution of license-plates using frames of low-resolution video[C]//2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS).2019:1-6.
[72]NISHIBORI K,TAKAHASHI T,DEGUCHI D,et al.Exem-plar-based human body super-resolution for surveillance camera systems[C]//2014 International Conference on Computer Vision Theory and Applications (VISAPP).IEEE,2014:115-121.
[73]LEE Y,YUN J W,HONG Y,et al.Accurate license plate recognition and super-resolution using a generative adversarial networks on traffic surveillance video[C]//2018 IEEE InternationalConference on Consumer Electronics-Asia (ICCE-Asia).IEEE,2018:1-4.
[74]REN S,LI J,GUO K,et al.Medical video super-resolution based on asymmetric back-projection network with multilevel error feedback[J].IEEE Access,2021,9:17909-17920.
[75]BONANNO D,DEBONO C J.A Medical Video Coding Scheme with Preserved Diagnostic Quality[C]//2019 IEEE Global Communications Conference (GLOBECOM).IEEE,2019:1-6.
[76]XIAO A,WANG Z,WANG L,et al.Super-resolution for “Jilin-1” satellite video imagery via a convolutional network[J].Sensors,2018,18(4):1194.
[77]GARCIA D C,FONSECA T A,QUEIROZ R L D.Example-based super-resolution for point-cloud video[C]//2018 25th IEEE International Conference on Image Processing (ICIP).IEEE,2018:2959-2963.
[78]MATSUSHITA Y,KAWASAKI H,ONO S,et al.Simultaneous deblur and super-resolution technique for video sequence captured by hand-held video camera[C]//2014 IEEE International Conference on Image Processing (ICIP).IEEE,2014:4562-4566.
[1] 胡伏原, 万新军, 沈鸣飞, 徐江浪, 姚睿, 陶重犇.
深度卷积神经网络图像实例分割方法研究进展
Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network
计算机科学, 2022, 49(5): 10-24. https://doi.org/10.11896/jsjkx.210200038
[2] 徐化池, 史殿习, 崔玉宁, 景罗希, 刘聪.
面向事件相机的时间信息融合网络框架
Time Information Integration Network for Event Cameras
计算机科学, 2022, 49(5): 43-49. https://doi.org/10.11896/jsjkx.210400047
[3] 张文轩, 吴秦.
基于多分支注意力增强的细粒度图像分类
Fine-grained Image Classification Based on Multi-branch Attention-augmentation
计算机科学, 2022, 49(5): 105-112. https://doi.org/10.11896/jsjkx.210100108
[4] 董奇达, 王喆, 吴松洋.
结合注意力机制与几何信息的特征融合框架
Feature Fusion Framework Combining Attention Mechanism and Geometric Information
计算机科学, 2022, 49(5): 129-134. https://doi.org/10.11896/jsjkx.210300180
[5] 赵人行, 徐频捷, 刘瑶.
基于深度卷积残差网络的心电单导联房颤检测方法
ECG-based Atrial Fibrillation Detection Based on Deep Convolutional Residual Neural Network
计算机科学, 2022, 49(5): 186-193. https://doi.org/10.11896/jsjkx.220200002
[6] 钟将, 尹红, 张剑.
基于学术知识图谱的辅助创新技术研究
Academic Knowledge Graph-based Research for Auxiliary Innovation Technology
计算机科学, 2022, 49(5): 194-199. https://doi.org/10.11896/jsjkx.210400195
[7] 李子仪, 周夏冰, 王中卿, 张民.
基于用户关联的立场检测
Stance Detection Based on User Connection
计算机科学, 2022, 49(5): 221-226. https://doi.org/10.11896/jsjkx.210400135
[8] 焦翔, 魏祥麟, 薛羽, 王超, 段强.
基于深度学习的自动调制识别研究
Automatic Modulation Recognition Based on Deep Learning
计算机科学, 2022, 49(5): 266-278. https://doi.org/10.11896/jsjkx.211000085
[9] 刘林云, 陈开颜, 李雄伟, 张阳, 谢方方.
基于卷积神经网络的旁路密码分析综述
Overview of Side Channel Analysis Based on Convolutional Neural Network
计算机科学, 2022, 49(5): 296-302. https://doi.org/10.11896/jsjkx.210300286
[10] 胡志濠, 潘祖烈.
基于QRNN的网络协议模糊测试用例过滤方法
Testcase Filtering Method Based on QRNN for Network Protocol Fuzzing
计算机科学, 2022, 49(5): 318-324. https://doi.org/10.11896/jsjkx.210300281
[11] 高越, 傅湘玲, 欧阳天雄, 陈松龄, 闫晨巍.
基于时空自适应图卷积神经网络的脑电信号情绪识别
EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network
计算机科学, 2022, 49(4): 30-36. https://doi.org/10.11896/jsjkx.210900200
[12] 么晓明, 丁世昌, 赵涛, 黄宏, 罗家德, 傅晓明.
大数据驱动的社会经济地位分析研究综述
Big Data-driven Based Socioeconomic Status Analysis:A Survey
计算机科学, 2022, 49(4): 80-87. https://doi.org/10.11896/jsjkx.211100014
[13] 曹合心, 赵亮, 李雪峰.
图神经网络在Text-to-SQL解析中的技术研究
Technical Research of Graph Neural Network for Text-to-SQL Parsing
计算机科学, 2022, 49(4): 110-115. https://doi.org/10.11896/jsjkx.210200173
[14] 窦智, 王宁, 王世杰, 王智慧, 李豪杰.
结合绘画先验的线稿上色方法
Sketch Colorization Method with Drawing Prior
计算机科学, 2022, 49(4): 195-202. https://doi.org/10.11896/jsjkx.210300140
[15] 赵凯, 安卫超, 张晓宇, 王彬, 张杉, 相洁.
共享浅层参数多任务学习的脑出血图像分割与分类
Intracerebral Hemorrhage Image Segmentation and Classification Based on Multi-taskLearning of Shared Shallow Parameters
计算机科学, 2022, 49(4): 203-208. https://doi.org/10.11896/jsjkx.201000153
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[2] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[3] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[4] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[5] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[6] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[7] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[8] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .
[9] 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案[J]. 计算机科学, 2018, 45(4): 122 -125 .
[10] 杨羽琦,章国安,金喜龙. 车载自组织网络中基于车辆密度的双簇头路由协议[J]. 计算机科学, 2018, 45(4): 126 -130 .