World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5537
World Ranking
12551
National Ranking
5092

Jun Xu 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 Jun Xu 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: 47 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Jun Xu 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 Jun Xu 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Jun Xu is affiliated with the University of Utah in the United States and has contributed substantially to research in computer science and engineering, particularly focusing on computer vision and pattern recognition.

Their research spans various subfields including computer vision and pattern recognition, artificial intelligence, media technology, radiology, nuclear medicine and imaging, and biomedical engineering. Their work covers a range of topics such as advanced image processing techniques, image enhancement techniques, image and signal denoising methods, visual attention and saliency detection, advanced image and video retrieval techniques, advanced vision and imaging, and advanced X-ray and CT imaging.

Jun Xu has published extensively, with a notable presence in several prominent venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Medical Imaging

Significant recent papers authored or co-authored by Jun Xu include:

  • "Deep Hough Transform for Semantic Line Detection" (2021), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "CDNet: Complementary Depth Network for RGB-D Salient Object Detection" (2021), published in IEEE Transactions on Image Processing
  • "MobileSal: Extremely Efficient RGB-D Salient Object Detection" (2021), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Conditional Variational Image Deraining" (2020), published in IEEE Transactions on Image Processing
  • "PID Controller-Based Stochastic Optimization Acceleration for Deep Neural Networks" (2020), published in IEEE Transactions on Neural Networks and Learning Systems

Their collaborative work involves frequent co-authorship with colleagues such as Xiantong Zhen, Ming-Ming Cheng, Ling Shao, Yingjun Du, and Dinggang Shen, reflecting ongoing partnerships in advancing their research areas.

Best Publications

  • JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

    Yu-Huan Wu;Shang-Hua Gao;Jie Mei;Jun Xu

  • STAR: A Structure and Texture Aware Retinex Model

    Jun Xu;Yingkun Hou;Dongwei Ren;Li Liu

  • Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

    Jun Xu;Lei Zhang;Wangmeng Zuo;David Zhang

  • Bilateral Attention Network for RGB-D Salient Object Detection

    Zhao Zhang;Zheng Lin;Jun Xu;Wen-Da Jin

  • Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

    Jun Xu;Lei Zhang;David Zhang;Xiangchu Feng

  • A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

    Jun Xu;Lei Zhang;David Zhang

  • RANet: Ranking Attention Network for Fast Video Object Segmentation

    Ziqin Wang;Jun Xu;Li Liu;Fan Zhu

  • Real-world Noisy Image Denoising: A New Benchmark.

    Jun Xu;Hui Li;Zhetong Liang;David Zhang

  • Deep Hough Transform for Semantic Line Detection.

    Kai Zhao;Qi Han;Chang-Bin Zhang;Jun Xu

  • Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image

    Jun Xu;Yuan Huang;Ming-Ming Cheng;Li Liu

  • NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising

    Yingkun Hou;Jun Xu;Mingxia Liu;Guanghai Liu

  • A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping

    Zhetong Liang;Jun Xu;David Zhang;Zisheng Cao

  • CDNet: Complementary Depth Network for RGB-D Salient Object Detection

    Wen-Da Jin;Jun Xu;Qi Han;Yi Zhang

  • MobileSal: Extremely Efficient RGB-D Salient Object Detection.

    Yu-Huan Wu;Yun Liu;Jun Xu;Jia-Wang Bian

  • Sparse, collaborative, or nonnegative representation: Which helps pattern classification?

    Jun Xu;Wangpeng An;Lei Zhang;David Zhang;David Zhang

  • Noisy-as-Clean: Learning Self-Supervised Denoising From Corrupted Image

    Jun Xu;Yuan Huang;Ming-Ming Cheng;Li Liu

  • External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

    Jun Xu;Lei Zhang;David Zhang

  • Learning to Learn with Variational Information Bottleneck for Domain Generalization

    Yingjun Du;Jun Xu;Huan Xiong;Qiang Qiu

  • A PID Controller Approach for Stochastic Optimization of Deep Networks

    Wangpeng An;Wangpeng An;Haoqian Wang;Haoqian Wang;Qingyun Sun;Jun Xu

  • Gradient-Induced Co-Saliency Detection

    Zhao Zhang;Wenda Jin;Jun Xu;Ming-Ming Cheng

  • Temporal Modulation Network for Controllable Space-Time Video Super-Resolution

    Gang Xu;Jun Xu;Zhen Li;Liang Wang

Frequent Co-Authors

Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Ling Shao
Ling Shao Terminus International
David Zhang
David Zhang Chinese University of Hong Kong, Shenzhen
Ming-Ming Cheng
Ming-Ming Cheng Nankai University
Xiantong Zhen
Xiantong Zhen University of Amsterdam
Haoqian Wang
Haoqian Wang Tsinghua University
Li Liu
Li Liu Inception Institute of Artificial Intelligence
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology
Cees G. M. Snoek
Cees G. M. Snoek University of Amsterdam
Deng-Ping Fan
Deng-Ping Fan Nankai University

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