Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
20164.9k citationsJiwon Kim, Kyoung Mu Lee et al.profile →
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
20171.4k citationsSeungjun Nah, Kyoung Mu Lee et al.profile →
Visual tracking decomposition
2010865 citationsJunseok Kwon, Kyoung Mu Leeprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Kyoung Mu Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kyoung Mu Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyoung Mu Lee more than expected).
This network shows the impact of papers produced by Kyoung Mu Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kyoung Mu Lee. The network helps show where Kyoung Mu Lee may publish in the future.
Co-authorship network of co-authors of Kyoung Mu Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Kyoung Mu Lee.
A scholar is included among the top collaborators of Kyoung Mu Lee based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kyoung Mu Lee. Kyoung Mu Lee is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kwon, Junseok, et al.. (2017). Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space. arXiv (Cornell University).4 indexed citations
12.
Kim, Jiwon, et al.. (2016). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. 1646–1654.4904 indexed citations breakdown →
13.
Lee, Kyoung Mu, et al.. (2012). Combining multi-view stereo and super resolution in a unified framework. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–4.5 indexed citations
Kee, Seok-Cheol, Sang Uk Lee, & Kyoung Mu Lee. (1998). Illumination Invariant Face Recognition Using Photometric Stereo. Machine Vision and Applications. 83(7). 1466–1474.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.