World's Best Scientists 2026 revealed!
Chang-Su Kim

Chang-Su Kim

D-Index & Metrics

Computer Science

D-Index
47
Citations
10121
World Ranking
6434
National Ranking
51

Chang-Su Kim 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 Chang-Su Kim 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: 345 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Chang-Su Kim 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 Chang-Su Kim 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Chang-Su Kim is affiliated with Korea University in South Korea and specializes in research primarily within the fields of Computer Science and Engineering. Their work spans several subfields including Computer Vision and Pattern Recognition, Media Technology, Automotive Engineering, Signal Processing, and Artificial Intelligence.

The scientist has contributed extensively to areas such as Advanced Vision and Imaging, Advanced Image Processing Techniques, Image Processing Techniques and Applications, and Image Enhancement Techniques. Other notable topics include Advanced Image and Video Retrieval Techniques, Video Surveillance and Tracking Methods, and Image and Signal Denoising Methods.

Chang-Su Kim's recent scholarly publications highlight a focus on image and video analysis. Selected works include:

  • Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, 2021, published in the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Moving Window Regression: A Novel Approach to Ordinal Regression, 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Light Field Super-Resolution via Adaptive Feature Remixing, 2021, published in IEEE Transactions on Image Processing
  • Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes, 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Three-Dimensional Convolutional Neural Network for Prostate MRI Segmentation and Comparison of Prostate Volume Measurements by Use of Artificial Neural Network and Ellipsoid Formula, 2020, published in American Journal of Roentgenology

The frequent coauthors associated with Chang-Su Kim include Dongkwon Jin, Jae-Han Lee, Yeong Jun Koh, Keunsoo Ko, and Chul Lee. This collaboration is documented by multiple joint publications.

Common publication venues for their work comprise arXiv (Cornell University), IEEE Access, the Journal of Visual Communication and Image Representation, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and the 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Best Publications

  • Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

    Chulwoo Lee;Chul Lee;Chang-Su Kim

  • Optimized contrast enhancement for real-time image and video dehazing

    Jin-Hwan Kim;Won-Dong Jang;Jae-Young Sim;Chang-Su Kim

  • Technologies for 3D mesh compression: A survey

    Jingliang Peng;Chang-Su Kim;C. C. Jay Kuo

  • Motion-Compensated Frame Interpolation Using Bilateral Motion Estimation and Adaptive Overlapped Block Motion Compensation

    Byeong-Doo Choi;Jong-Woo Han;Chang-Su Kim;Sung-Jea Ko

  • Single-image deraining using an adaptive nonlocal means filter

    Jin-Hwan Kim;Chul Lee;Jae-Young Sim;Chang-Su Kim

  • Video Deraining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion

    Jin-Hwan Kim;Jae-Young Sim;Chang-Su Kim

  • Contrast enhancement based on layered difference representation

    Chulwoo Lee;Chul Lee;Chang-Su Kim

  • Power-Constrained Contrast Enhancement for Emissive Displays Based on Histogram Equalization

    Chulwoo Lee;Chul Lee;Young-Yoon Lee;Chang-Su Kim

  • Interactive Image Segmentation via Backpropagating Refinement Scheme

    Won-Dong Jang;Chang-Su Kim

  • Spatial and Temporal Error Concealment Techniques for Video Transmission Over Noisy Channels

    Wei-Ying Kung;Chang-Su Kim;C.-C.J. Kuo

  • Cross-border R&D alliances, absorptive capacity and technology learning

    Chang Su Kim;Andrew C. Inkpen

  • BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation

    Junheum Park;Keunsoo Ko;Chul Lee;Chang Su Kim

  • Primary Object Segmentation in Videos Based on Region Augmentation and Reduction

    Yeong Jun Koh;Chang-Su Kim

  • Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk With Restart

    Hansang Kim;Youngbae Kim;Jae-Young Sim;Chang-Su Kim

  • Single-Image Depth Estimation Based on Fourier Domain Analysis

    Jae-Han Lee;Minhyeok Heo;Kyung-Rae Kim;Chang-Su Kim

  • SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking

    Han-Ul Kim;Dae-Youn Lee;Jae-Young Sim;Chang-Su Kim

  • Asymmetric Bilateral Motion Estimation for Video Frame Interpolation

    Junheum Park;Chul Lee;Chang-Su Kim

  • Monocular Depth Estimation Using Relative Depth Maps

    Jae-Han Lee;Chang-Su Kim

  • Online Video Object Segmentation via Convolutional Trident Network

    Won-Dong Jang;Chang-Su Kim

  • Single image dehazing based on contrast enhancement

    Jin-Hwan Kim;Jae-Young Sim;Chang-Su Kim

Frequent Co-Authors

Sang Uk Lee
Sang Uk Lee Seoul National University
C.-C. Jay Kuo
C.-C. Jay Kuo University of Southern California
JongWon Kim
JongWon Kim Gwangju Institute of Science and Technology
Yo-Sung Ho
Yo-Sung Ho Gwangju Institute of Science and Technology
Sung-Jea Ko
Sung-Jea Ko Korea University
Soung Chang Liew
Soung Chang Liew Chinese University of Hong Kong
Anthony Vetro
Anthony Vetro Mitsubishi Electric (United States)
Minh N. Do
Minh N. Do University of Illinois at Urbana-Champaign
Kyoung Mu Lee
Kyoung Mu Lee Seoul National University

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