Ming-Jun Chen
Impact in
- Media Technology top 1%
- Advanced Image Fusion Techniques
- Advanced Optical Imaging Technologies
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- Image and Video Quality Assessment
- Visual Attention and Saliency Detection
- Advanced Image Processing Techniques
- Image Enhancement Techniques
Papers in
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- Image and Video Quality Assessment 5
- Advanced Image Processing Techniques 3
- Advanced Data Compression Techniques 1
- Image Enhancement Techniques 1
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- Advanced Image Fusion Techniques 3
- Co-authors
- Alan Conrad Bovik (5 shared papers)Lawrence K. Cormack (3 shared papers)Do-Kyoung Kwon (2 shared papers)Che-Chun Su (2 shared papers)Chia‐Wei Tsai (1 shared paper)Tzonelih Hwang (1 shared paper)Li‐Hua Gong (1 shared paper)
- Journals
- Journal of Modern Optics (1 paper)Optik (1 paper)IEEE Transactions on Image Processing (1 paper)Signal Processing Image Communication (1 paper)EURASIP Journal on Image and Video Processing (1 paper)
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Ming-Jun Chen
7 papers receiving 668 citations
Peers
Comparison fields: 5 of 28
- Media Technology 434
- Computer Vision and Pattern Recognition 651
- Cognitive Neuroscience 138
- Atomic and Molecular Physics, and Optics 78
- Computer Graphics and Computer-Aided Design 7
Countries citing papers authored by Ming-Jun Chen
This map shows the geographic impact of Ming-Jun Chen'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 Ming-Jun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Jun Chen more than expected).
Fields of papers citing papers by Ming-Jun Chen
This network shows the impact of papers produced by Ming-Jun Chen. 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 Ming-Jun Chen. The network helps show where Ming-Jun Chen may publish in the future.
Co-authors
The 7 scholars most cited alongside Ming-Jun Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 331 | |
| 2 | 2013 | 231 | |
| 3 | 2011 | 67 | |
| 4 | 2009 | 37 | |
| 5 | 2012 | 9 | |
| 6 | 2023 | 5 | |
| 7 | 2019 | 2 |
About Ming-Jun Chen
Ming-Jun Chen is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Cognitive Neuroscience, Atomic and Molecular Physics, and Optics and Artificial Intelligence, having authored 7 papers that have together received 682 indexed citations. Recurring topics across this work include Image and Video Quality Assessment (5 papers), Advanced Image Processing Techniques (3 papers), Advanced Image Fusion Techniques (3 papers), Visual perception and processing mechanisms (2 papers), Quantum Computing Algorithms and Architecture (1 paper), Advanced Data Compression Techniques (1 paper), Image Enhancement Techniques (1 paper) and Quantum Information and Cryptography (1 paper). The work is most often cited by research in Media Technology (434 citations), Computer Vision and Pattern Recognition (651 citations), Cognitive Neuroscience (138 citations), Atomic and Molecular Physics, and Optics (78 citations) and Computer Graphics and Computer-Aided Design (7 citations). Ming-Jun Chen has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Alan Conrad Bovik, Lawrence K. Cormack, Do-Kyoung Kwon, Che-Chun Su, Chia‐Wei Tsai, Tzonelih Hwang and Li‐Hua Gong. Their work appears in journals such as Journal of Modern Optics, Optik, IEEE Transactions on Image Processing, Signal Processing Image Communication and EURASIP Journal on Image and Video Processing.
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.