Draft:Kyoung Mu Lee
South Korean computer scientist
From Wikipedia, the free encyclopedia
Kyoung Mu Lee is a South Korean computer scientist and professor at Seoul National University (SNU).[1] His research specializes in computer vision, specifically image restoration, 3D human pose estimation, and visual tracking. Since 2021, he has served as the Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).[2] According to Google Scholar, his publications have received over 50,000 citations.[3]
 | Review waiting, please be patient.
This may take 2 months or more, since drafts are reviewed in no specific order. There are 4,282 pending submissions waiting for review.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
Reviewer tools
|
Submission declined on 7 April 2026 by ChrysGalley (talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
This draft has been resubmitted and is currently awaiting re-review. |  |
Comment: The COI also needs to be explicit here, see WP:COI. But we can't accept LLM drafts. ChrysGalley (talk) 08:32, 7 April 2026 (UTC)
- Comment: advisor Genesis0121 (talk) 07:58, 7 April 2026 (UTC)
 | The following Wikipedia contributor has declared a personal or professional connection to the subject of this page. Relevant policies and guidelines may include conflict of interest, autobiography, and neutral point of view.
|
University of Southern California (PhD)
Kyoung Mu Lee | |
|---|---|
| Citizenship | South Korean |
| Alma mater | Seoul National University (BS, MS) University of Southern California (PhD) |
| Awards | IEEE Fellow (2021), Samil Award (2022) |
| Scientific career | |
| Fields | Computer vision, Artificial Intelligence |
| Institutions | Seoul National University, Hongik University |
| Thesis | Shape from shading : models, algorithms and analysis (1993) |
| Doctoral advisor | C.-C. Jay Kuo |
| Website | cv |
Education and Career
Lee completed his B.S. and M.S. at Seoul National University in 1984 and 1986. He received a Ph.D. in Electrical Engineering from the University of Southern California in 1993.[1] His doctoral thesis, titled Shape from shading : models, algorithms and analysis, was supervised by C.-C. Jay Kuo.[4]
Lee was a faculty member at Hongik University from 1995 to 2003, after which he joined the Department of Electrical and Computer Engineering at SNU.[5] He held the position of Vice Dean of the College of Engineering between 2009 and 2011. He currently leads the SNU Computer Vision Laboratory and the Interdisciplinary Program in Artificial Intelligence.[6]
Research
Lee's research involves the application of deep learning to image restoration and 3D modeling.
Image Restoration and Super-Resolution
Lee's research group developed the VDSR (Very Deep Super-Resolution) algorithm in 2016, which utilized global skip-connections in deep convolutional neural networks.[7] In 2017, he co-authored the EDSR (Enhanced Deep Residual Networks) architecture. This model won the NTIRE 2017 Single Image Super-Resolution Challenge and has received over 10,000 citations.[8][3]
In 2017, Lee introduced the GOPRO dataset, a benchmark for non-uniform deblurring collected using high-speed cameras to provide realistic motion blur pairs.[9]
3D Pose Estimation
Lee developed V2V-PoseNet, a voxel-to-voxel network for 3D human and hand pose estimation.[10] His lab released the InterHand2.6M dataset in 2020 for 3D interacting hand pose benchmarks.[6]
Professional Leadership
Lee is the first Asian-based scholar to serve as Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence.[2] He was the General Chair for the 2019 International Conference on Computer Vision (ICCV) in Seoul and ACM Multimedia 2018.[11] He founded the Korean Conference on Computer Vision (KCCV) and served as President of the Korean Computer Vision Society (KCVS).[12]
Awards and Honors
- Fellow, Institute of Electrical and Electronics Engineers (IEEE), 2021[13]
- 63rd Samil Prize (Academic Category), 2022[14]
- Member, Korean Academy of Science and Technology (KAST)
- Member, National Academy of Engineering of Korea (NAEK)
Selected Publications
- Lim, B; Son, S; Kim, H; Nah, S; Lee, KM (2017). "Enhanced Deep Residual Networks for Single Image Super-Resolution" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
- Kim, J; Lee, JK; Lee, KM (2016). "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- Nah, S; Kim, TH; Lee, KM (2017). "Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
LLM-generated pages with the below issues may be deleted without notice.
These tools are prone to specific issues that violate our policies:
Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject.
See the advice page on large language models for more information.