World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
6219
World Ranking
9787
National Ranking
104

Nam Ik Cho 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 Nam Ik Cho 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 347 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.

Nam Ik Cho 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 Nam Ik Cho sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Nam Ik Cho is a researcher affiliated with Seoul National University in South Korea. Their work is primarily situated within the fields of Computer Science and Engineering, with a significant focus on Computer Vision and Pattern Recognition.

Their research covers various specialized areas including Advanced Image Processing Techniques, Advanced Vision and Imaging, Image and Signal Denoising Methods, Image Processing Techniques and Applications, and Image Enhancement Techniques. Additional areas of interest include Advanced Image and Video Retrieval Techniques and Image Retrieval and Classification Techniques.

Nam Ik Cho has contributed extensively to academic literature, with frequent publications in multiple venues. The most common venues for their work include:

  • arXiv (Cornell University)
  • IEEE Access
  • Multimedia Tools and Applications
  • IEEE Signal Processing Letters
  • 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Their collaborative network includes frequent coauthors such as Jae Woong Soh, Hyung Il Koo, Young Kyun Jang, Yeong Il Jang, and Hochang Rhee. These collaborations suggest a strong engagement in team research within their field.

The following are some of Nam Ik Cho's recent papers, highlighting their contribution to image processing and computer vision topics:

  • "A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution," 2021, IEEE Transactions on Multimedia
  • "Self-supervised Product Quantization for Deep Unsupervised Image Retrieval," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Variational Deep Image Restoration," 2022, IEEE Transactions on Image Processing
  • "Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter," 2020, IEEE Access
  • "AGARNet: Adaptively Gated JPEG Compression Artifacts Removal Network for a Wide Range Quality Factor," 2020, IEEE Access

Best Publications

  • Fast algorithm and implementation of 2-D discrete cosine transform

    Nam Ik Cho;San Uk Lee

  • Meta-Transfer Learning for Zero-Shot Super-Resolution

    Jae Woong Soh;Sunwoo Cho;Nam Ik Cho

  • Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization

    Yoonsik Kim;Jae Woong Soh;Gu Yong Park;Nam Ik Cho

  • On the adaptive lattice notch filter for the detection of sinusoids

    N.I. Cho;S.U. Lee

  • Adaptive line enhancement by using an IIR lattice notch filter

    Nam Ik Cho;Chong-Ho Choi;Sang Uk Lee

  • A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

    Unknown

  • Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination

    Jae Woong Soh;Gu Yong Park;Junho Jo;Nam Ik Cho

  • Improved linear soft-input soft-output detection via soft feedback successive interference cancellation

    Jun Won Choi;A.C. Singer;Jungwoo Lee;Nam Ik Cho

  • DCT algorithms for VLSI parallel implementations

    Nam Ik Cho;Sang Uk Lee

  • Apparatus and method for adapting 2d and 3d stereoscopic video signal

    Jeho Nam;Man Bae Kim;Jin Woo Hong;Jin Woong Kim

  • METHOD FOR PHOTOGRAPHING PANORAMA MOSAICS PICTURE IN MOBILE DEVICE

    Park Kyoung Ju;Cho Sung Dae;Kim Soo Kyun;Moon Jae Won

  • A fast 4*4 DCT algorithm for the recursive 2-D DCT

    N.I. Cho;S.U. Lee

  • A Multi-Exposure Image Fusion Based on the Adaptive Weights Reflecting the Relative Pixel Intensity and Global Gradient

    Sang-hoon Lee;Jae Sung Park;Nam Ik Cho

  • Suppression of narrow-band interference in DS-Spread spectrum systems using adaptive IIR Notch filter

    Jun Won Choi;Nam Ik Cho

  • Panorama Mosaic Optimization for Mobile Camera Systems

    Seong Jong Ha;Hyung Koo;Sang Hwa Lee;Nam Ik Cho

  • PuVAE: A Variational Autoencoder to Purify Adversarial Examples

    Uiwon Hwang;Jaewoo Park;Hyemi Jang;Sungroh Yoon

  • Language-Independent Text-Line Extraction Algorithm for Handwritten Documents

    Jewoong Ryu;Hyung Il Koo;Nam Ik Cho

  • Variational Deep Image Restoration

    Unknown

  • Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression

    Seyun Kim;Nam Ik Cho

  • Intra prediction method based on the linear relationship between the channels for YUV 4∶2∶0 intra coding

    Sang Heon Lee;Nam Ik Cho

  • Text-Line Extraction in Handwritten Chinese Documents Based on an Energy Minimization Framework

    Hyung Il Koo;Nam Ik Cho

  • Fixed-point error analysis of CORDIC processor based on the variance propagation formula

    Sang Yoon Park;Nam Ik Cho

Frequent Co-Authors

Sang Uk Lee
Sang Uk Lee Seoul National University
Andrew C. Singer
Andrew C. Singer University of Illinois at Urbana-Champaign
Byonghyo Shim
Byonghyo Shim Seoul National University
Sanjit K. Mitra
Sanjit K. Mitra University of California, Santa Barbara
Sungroh Yoon
Sungroh Yoon Seoul National University
Naehyuck Chang
Naehyuck Chang Korea Advanced Institute of Science and Technology
Eunji Lee
Eunji Lee Gwangju Institute of Science and Technology
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University
Heung Nam Han
Heung Nam Han Seoul National University
Kyu Hwan Oh
Kyu Hwan Oh Seoul National University

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Related Online Degrees & Career Pathways

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