Lean Fu
Impact in
- Media Technology top 2%
- Image Processing Techniques and Applications
- Advanced Image Fusion Techniques
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- Advanced Image Processing Techniques
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
- Image Enhancement Techniques
- Generative Adversarial Networks and Image Synthesis
- Face recognition and analysis
Papers in
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- Advanced Image Processing Techniques 5
- Image and Signal Denoising Methods 3
- Advanced Vision and Imaging 3
- Digital Media Forensic Detection 1
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- Image Processing Techniques and Applications 2
- Advanced Image Fusion Techniques 1
- Co-authors
- Ding Liu (2 shared papers)Fangmin Chen (2 shared papers)Fangyuan Kong (1 shared paper)Mingxi Li (1 shared paper)Songwei Liu (1 shared paper)Jingwen He (1 shared paper)Yang Bai (1 shared paper)Jie Tang (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- China
In The Last Decade
Lean Fu
5 papers receiving 287 citations
Lean Fu's Hit Papers
Peers
Comparison fields: 5 of 35
- Media Technology 139
- Computer Vision and Pattern Recognition 272
- Instrumentation 6
- Acoustics and Ultrasonics 1
- Computer Graphics and Computer-Aided Design 3
Countries citing papers authored by Lean Fu
This map shows the geographic impact of Lean Fu'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 Lean Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lean Fu more than expected).
Fields of papers citing papers by Lean Fu
This network shows the impact of papers produced by Lean Fu. 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 Lean Fu. The network helps show where Lean Fu may publish in the future.
Co-authors
The 21 scholars most cited alongside Lean Fu, 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 | Residual Local Feature Network for Efficient Super-Resolution Hit paper breakdown → | 2022 | 150 |
| 2 | 2022 | 65 | |
| 3 | 2022 | 50 | |
| 4 | 2022 | 26 | |
| 5 | 2023 | 5 | |
| 6 | 2024 | 0 |
About Lean Fu
Lean Fu is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Infectious Diseases, Organic Chemistry and Surgery, having authored 6 papers that have together received 296 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Image and Signal Denoising Methods (3 papers), Advanced Vision and Imaging (3 papers), Image Processing Techniques and Applications (2 papers), Digital Media Forensic Detection (1 paper) and Advanced Image Fusion Techniques (1 paper). The work is most often cited by research in Media Technology (139 citations), Computer Vision and Pattern Recognition (272 citations), Instrumentation (6 citations), Acoustics and Ultrasonics (1 citation) and Computer Graphics and Computer-Aided Design (3 citations). Lean Fu has collaborated with scholars based in China. Frequent co-authors include Ding Liu, Fangmin Chen, Fangyuan Kong, Mingxi Li, Songwei Liu, Jingwen He, Yang Bai, Jie Tang, Gangshan Wu and Jie Liu. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.