Shuzhou Yang
- Computer Vision and Pattern Recognition top 2%
- Media Technology top 2%
- Oceanography
- Biomedical Engineering
- Artificial Intelligence
- Topics
- Image Enhancement Techniques (7 papers)Advanced Image Processing Techniques (6 papers)Image and Signal Denoising Methods (3 papers)
- Journals
- IEEE Transactions on Image ProcessingIEEE Transactions on Medical ImagingPattern Recognition
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Shuzhou Yang
9 papers receiving 640 citations
Hit Papers
Peers
Comparison fields: 5 of 59
- Computer Vision and Pattern Recognition 584
- Media Technology 197
- Oceanography 39
- Biomedical Engineering 38
- Artificial Intelligence 31
Countries citing papers authored by Shuzhou Yang
This map shows the geographic impact of Shuzhou Yang'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 Shuzhou Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuzhou Yang more than expected).
Fields of papers citing papers by Shuzhou Yang
This network shows the impact of papers produced by Shuzhou Yang. 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 Shuzhou Yang. The network helps show where Shuzhou Yang may publish in the future.
Co-authorship network of co-authors of Shuzhou Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Shuzhou Yang. A scholar is included among the top collaborators of Shuzhou Yang 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 Shuzhou Yang. Shuzhou Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentationbreakdown → | 53 |
| 3 | 7 | |
| 4 | 24 | |
| 5 | 8 | |
| 6 | Implicit Neural Representation for Cooperative Low-light Image Enhancementbreakdown → | 111 |
| 7 | 10 | |
| 8 | Target Oriented Perceptual Adversarial Fusion Network for Underwater Image Enhancementbreakdown → | 197 |
| 9 | Twin Adversarial Contrastive Learning for Underwater Image Enhancement and Beyondbreakdown → | 234 |
About Shuzhou Yang
Shuzhou Yang is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Neurology, having authored 9 papers that have together received 649 indexed citations. Recurring topics across this work include Image Enhancement Techniques (7 papers), Advanced Image Processing Techniques (6 papers) and Image and Signal Denoising Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (584 citations), Media Technology (197 citations) and Instrumentation (16 citations). Shuzhou Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xin Fan, Risheng Liu, Zhiying Jiang, Zhuoxiao Li, Zihan Li, Jian Zhang, Yanmin Wu, Dinggang Shen, Beizhan Wang and Yuan‐Ting Zhang. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging and Pattern Recognition.
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.