World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
12212
World Ranking
3862
National Ranking
518

Junyu Dong publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Junyu Dong sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 482 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Junyu Dong D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Junyu Dong sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Junyu Dong is affiliated with the Ocean University of China. Their research spans primarily the fields of Computer Science and Engineering, with a significant focus on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Oceanography, and Atmospheric Science.

The scientist's work includes contributions on a variety of topics, notably:

  • Advanced Vision and Imaging
  • Remote-Sensing Image Classification
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications
  • Image Enhancement Techniques
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image Fusion Techniques

Junyu Dong has published extensively, with a frequent presence in leading scientific venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Geoscience and Remote Sensing
  • Knowledge-Based Systems
  • IEEE Geoscience and Remote Sensing Letters

The list of recent papers by Junyu Dong demonstrates a wide range of interests:

  • "Effects of heavy metals on microbial communities in sediments and establishment of bioindicators based on microbial taxa and function for environmental monitoring and management," 2020, The Science of The Total Environment
  • "Underwater image processing and analysis: A review," 2020, Signal Processing Image Communication
  • "Enhancing MOEA/D with information feedback models for large-scale many-objective optimization," 2020, Information Sciences
  • "Perceptual Underwater Image Enhancement With Deep Learning and Physical Priors," 2020, IEEE Transactions on Circuits and Systems for Video Technology
  • "SWIPENET: Object detection in noisy underwater scenes," 2022, Pattern Recognition

Collaboration is a substantial aspect of their professional activity. Frequent coauthors include:

  • Feng Gao
  • Hui Yu
  • Qian Du
  • Xin Sun
  • Huiyu Zhou

Best Publications

  • Prediction of Sea Surface Temperature Using Long Short-Term Memory

    Qin Zhang;Hui Wang;Junyu Dong;Guoqiang Zhong

  • Underwater image enhancement via extended multi-scale Retinex

    Shu Zhang;Ting Wang;Junyu Dong;Hui Yu

  • Stock Market Prediction Based on Generative Adversarial Network

    Kang Zhang;Guoqiang Zhong;Junyu Dong;Shengke Wang

  • Curricular Contrastive Regularization for Physics-Aware Single Image Dehazing

    Unknown

  • A CFCC-LSTM Model for Sea Surface Temperature Prediction

    Yuting Yang;Junyu Dong;Xin Sun;Estanislau Lima

  • Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet

    Feng Gao;Junyu Dong;Bo Li;Qizhi Xu

  • An overview on data representation learning: From traditional feature learning to recent deep learning

    Guoqiang Zhong;Li-Na Wang;Xiao Ling;Junyu Dong

  • Behavior of crossover operators in NSGA-III for large-scale optimization problems

    Jiao-Hong Yi;Jiao-Hong Yi;Li-Ning Xing;Gai-Ge Wang;Junyu Dong

  • Underwater image processing and analysis: A review

    Muwei Jian;Muwei Jian;Muwei Jian;Xiangyu Liu;Hanjiang Luo;Xiangwei Lu

  • An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems

    Jiao-Hong Yi;Suash Deb;Junyu Dong;Amir Hossein Alavi

  • Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks

    Feng Gao;Xiao Wang;Yunhao Gao;Junyu Dong

  • Enhancing MOEA/D with information feedback models for large-scale many-objective optimization

    Yin Zhang;Gai-Ge Wang;Keqin Li;Wei-Chang Yeh

  • RRNet: A Hybrid Detector for Object Detection in Drone-Captured Images

    Changrui Chen;Yu Zhang;Qingxuan Lv;Shuo Wei

  • Visual-Patch-Attention-Aware Saliency Detection

    Muwei Jian;Kin-Man Lam;Junyu Dong;Linlin Shen

  • Perceptual Underwater Image Enhancement With Deep Learning and Physical Priors

    Long Chen;Zheheng Jiang;Lei Tong;Zhihua Liu

  • A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data

    Aite Zhao;Lin Qi;Lin Qi;Jie Li;Junyu Dong

  • Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

    Feng Gao;Junyu Dong;Bo Li;Qizhi Xu

  • Human fall detection in surveillance video based on PCANet

    Shengke Wang;Long Chen;Zixi Zhou;Xin Sun

  • SWIPENET: Object detection in noisy underwater scenes

    Unknown

  • Underwater object detection using Invert Multi-Class Adaboost with deep learning

    Long Chen;Zhihua Liu;Lei Tong;Zheheng Jiang

  • Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection

    Muwei Jian;Muwei Jian;Qiang Qi;Junyu Dong;Yilong Yin

  • Capture and Synthesis of 3D Surface Texture

    Junyu Dong;Mike Chantler

  • Transferring deep knowledge for object recognition in Low-quality underwater videos

    Xin Sun;Junyu Shi;Lipeng Liu;Junyu Dong

Frequent Co-Authors

Muwei Jian
Muwei Jian Shandong University of Finance and Economics
Hui Yu
Hui Yu University of Portsmouth
Huiyu Zhou
Huiyu Zhou University of Leicester
Kin-Man Lam
Kin-Man Lam Hong Kong Polytechnic University
Yilong Yin
Yilong Yin Shandong University
Gai-Ge Wang
Gai-Ge Wang Ocean University of China
Honghai Liu
Honghai Liu University of Portsmouth
Qian Du
Qian Du Mississippi State University
Kaizhu Huang
Kaizhu Huang Duke Kunshan University
Sheng Chen
Sheng Chen University of Southampton

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