Jun Han

1.3k citations
83 papers · 848 · h-index 17

Impact in

Papers in

Jun Han

77 papers receiving 815 citations

Peers

Jun Han
Comparison fields: 5 of 113
  • Computer Graphics and Computer-Aided Design 155
  • Computer Vision and Pattern Recognition 415
  • Signal Processing 80
  • Computer Science Applications 30
  • Artificial Intelligence 172
Replace Paul Rosen with:
Paul Rosen United States
Mario Lučić United States
Yijun Li United States
Ching-Kuang Shene United States
Faisal Z. Qureshi Canada
Ioannis Mitliagkas United States
Kalpathi Subramanian United States
Feng Sun China
Feng Qiu United States
Jun Han relative to Paul Rosen United States Paul Rosen's profile →
Citations per field
00.5×2.6×
Paul Rosen · 1×
Citations per year

Countries citing papers authored by Jun Han

Since Specialization
Citations

This map shows the geographic impact of Jun Han'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 Jun Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Han more than expected).

Fields of papers citing papers by Jun Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Han. 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 Jun Han. The network helps show where Jun Han may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Han Line = papers co-authored together Jun Han links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 83 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201870
2 201955
3 201949
4 202042
5 201041
6 202040
7 202239
8 202136
9 202032
10 201932
11 202231
12 200228
13 202326
14 202122
15 201919
16 202117
17 202216
18 201914
19 201913
20 201412

About Jun Han

Jun Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Control and Systems Engineering and Computational Mechanics, having authored 83 papers that have together received 848 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (13 papers), Advanced Vision and Imaging (11 papers), Generative Adversarial Networks and Image Synthesis (10 papers), Image and Signal Denoising Methods (8 papers), Fault Detection and Control Systems (7 papers), Higher Education and Teaching Methods (7 papers), Flow Measurement and Analysis (6 papers) and Data Visualization and Analytics (5 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (155 citations), Computer Vision and Pattern Recognition (415 citations), Signal Processing (80 citations), Computer Science Applications (30 citations) and Artificial Intelligence (172 citations). Jun Han has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Chaoli Wang, Danny Z. Chen, Jun Tao, Hao Zheng, Xuefei Chen, Zhuo Zhao, Jing Liu, Lin Yang, Hao Zheng and Yizhe Zhang. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, Scientific Reports, Journal of Vision and Frontiers in Pharmacology.

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.

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