Lean Fu
Impact in
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- Advanced Image Processing Techniques
- Generative Adversarial Networks and Image Synthesis
- Image Enhancement Techniques
- Advanced Neural Network Applications
- Image and Video Quality Assessment
- Image and Signal Denoising Methods
- Face recognition and analysis
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- Advanced Image Fusion Techniques
Papers in
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- Advanced Image Processing Techniques 6
- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Ding Liu (3 shared papers)Fangmin Chen (2 shared papers)Jingwen He (1 shared paper)Yang Bai (1 shared paper)Songwei Liu (1 shared paper)Mingxi Li (1 shared paper)Gangshan Wu (1 shared paper)Jie Liu (1 shared paper)
- Journals
- Lecture notes in computer science (2 papers)ACM International Conference on Multimedia (ACM MM) (1 paper)neural information processing systems (1 paper)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (1 paper)IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2 papers)
- Partner nations
- China
In The Last Decade
Lean Fu
8 papers receiving 383 citations
Lean Fu's Hit Papers
Peers
Comparison fields: 5 of 26
- Computer Vision and Pattern Recognition 379
- Media Technology 10
- Acoustics and Ultrasonics 1
- Signal Processing 6
- Health Informatics 1
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 25 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 | 191 |
| 2 | 2022 | 94 | |
| 3 | 2022 | 58 | |
| 4 | 2022 | 38 | |
| 5 | 2023 | 9 | |
| 6 | 2023 | 4 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 1 |
About Lean Fu
Lean Fu is a scholar working on Computer Vision and Pattern Recognition, Nuclear Energy and Engineering, Waste Management and Disposal, Infectious Diseases and Organic Chemistry, having authored 8 papers that have together received 397 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (6 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (379 citations), Media Technology (10 citations), Acoustics and Ultrasonics (1 citation), Signal Processing (6 citations) and Health Informatics (1 citation). Lean Fu has collaborated with scholars based in China. Frequent co-authors include Ding Liu, Fangmin Chen, Jingwen He, Yang Bai, Songwei Liu, Mingxi Li, Gangshan Wu, Jie Liu, Shi Wu and Chao Dong. Their work appears in journals such as Lecture notes in computer science, ACM International Conference on Multimedia (ACM MM), neural information processing systems, IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings and IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.