World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
5987
World Ranking
11199
National Ranking
4636

Hua Huang 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 Hua Huang 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: 136 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Hua Huang 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 Hua Huang 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Hua Huang is affiliated with the University of California, Merced in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a strong emphasis on subfields such as Computer Vision and Pattern Recognition, Media Technology, Biomedical Engineering, Artificial Intelligence, and Electrical and Electronic Engineering.

Their body of work encompasses a broad range of topics, including:

  • Image and Signal Denoising Methods
  • Advanced Image Fusion Techniques
  • Advanced Image Processing Techniques
  • Advanced Vision and Imaging
  • Remote-Sensing Image Classification
  • Video Surveillance and Tracking Methods
  • Image Processing Techniques and Applications

Hua Huang has published extensively, with frequent contributions to venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Geoscience and Remote Sensing
  • IEEE Transactions on Image Processing
  • Neurocomputing

They have collaborated regularly with a group of co-authors, including Lizhi Wang, Lei Zhang, Yuanfei Huang, Lingfei Song, and Hansen Feng.

Some recent papers highlight the diversity and focus of Hua Huang's research:

  • "3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising," published in 2020 in IEEE Transactions on Neural Networks and Learning Systems
  • "NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results," published in 2022 at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • "Coded Hyperspectral Image Reconstruction using Deep External and Internal Learning," published in 2021 in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Small Target Detection in Infrared Videos Based on Spatio-Temporal Tensor Model," published in 2020 in IEEE Transactions on Geoscience and Remote Sensing
  • "SIND: A Drone Dataset at Signalized Intersection in China," published in 2022 at the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Best Publications

  • No-reference image quality assessment based on spatial and spectral entropies

    Lixiong Liu;Bao Liu;Hua Huang;Alan Conrad Bovik

  • Blind image quality assessment by relative gradient statistics and adaboosting neural network

    Lixiong Liu;Yi Hua;Qingjie Zhao;Hua Huang

  • Occlusion-Aware Real-Time Object Tracking

    Xingping Dong;Jianbing Shen;Dajiang Yu;Wenguan Wang

  • Super-resolution of human face image using canonical correlation analysis

    Hua Huang;Huiting He;Xin Fan;Junping Zhang

  • A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising

    Kaixuan Wei;Ying Fu;Jiaolong Yang;Hua Huang

  • No-reference image quality assessment in curvelet domain

    Lixiong Liu;Hongping Dong;Hua Huang;Alan C. Bovik

  • 3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

    Kaixuan Wei;Ying Fu;Hua Huang

  • Neighbor embedding based super-resolution algorithm through edge detection and feature selection

    Tak-Ming Chan;Junping Zhang;Jian Pu;Hua Huang

  • Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior

    Lizhi Wang;Chen Sun;Ying Fu;Min H. Kim

  • Incremental Learning Using Conditional Adversarial Networks

    Ye Xiang;Ying Fu;Pan Ji;Hua Huang

  • HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging

    Lizhi Wang;Tao Zhang;Ying Fu;Hua Huang

  • Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features

    Hua Huang;Huiting He

  • Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements

    Kaixuan Wei;Jiaolong Yang;Ying Fu;David Wipf

  • DNU: Deep Non-Local Unrolling for Computational Spectral Imaging

    Lizhi Wang;Chen Sun;Maoqing Zhang;Ying Fu

  • Hyperspectral Image Super-Resolution With Optimized RGB Guidance

    Ying Fu;Tao Zhang;Yinqiang Zheng;Debing Zhang

  • Arcimboldo-like collage using internet images

    Hua Huang;Lei Zhang;Hong-Chao Zhang

  • Coded Hyperspectral Image Reconstruction using Deep External and Internal Learning.

    Ying Fu;Tao Zhang;Lizhi Wang;Hua Huang

  • Binocular spatial activity and reverse saliency driven no-reference stereopair quality assessment

    Lixiong Liu;Bao Liu;Che-Chun Su;Hua Huang

  • Small Target Detection in Infrared Videos Based on Spatio-Temporal Tensor Model

    Hong-Kang Liu;Lei Zhang;Hua Huang

  • Spectral Reflectance Recovery From a Single RGB Image

    Ying Fu;Yongrong Zheng;Lin Zhang;Hua Huang

  • Fast Facial Image Super-Resolution via Local Linear Transformations for Resource-Limited Applications

    Hua Huang;Ning Wu

  • Computational Hyperspectral Imaging Based on Dimension-Discriminative Low-Rank Tensor Recovery

    Shipeng Zhang;Lizhi Wang;Ying Fu;Xiaoming Zhong

Frequent Co-Authors

Ying Fu
Ying Fu Beijing Institute of Technology
Yinqiang Zheng
Yinqiang Zheng National Institute of Informatics
Alan C. Bovik
Alan C. Bovik The University of Texas at Austin
Carola-Bibiane Schönlieb
Carola-Bibiane Schönlieb University of Cambridge
Xin Fan
Xin Fan Dalian University of Technology
Z. Jane Wang
Z. Jane Wang University of British Columbia
Paul L. Rosin
Paul L. Rosin Cardiff University
David Wipf
David Wipf Amazon (United States)
Tak-Ming Chan
Tak-Ming Chan Hong Kong Polytechnic University
Wenguan Wang
Wenguan Wang Zhejiang University

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