Zhenqi Fu

990 total citations · 1 hit paper
27 papers, 513 citations indexed

About

Zhenqi Fu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Zhenqi Fu has authored 27 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 7 papers in Media Technology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Zhenqi Fu's work include Image Enhancement Techniques (13 papers), Advanced Image Processing Techniques (7 papers) and Advanced Image Fusion Techniques (6 papers). Zhenqi Fu is often cited by papers focused on Image Enhancement Techniques (13 papers), Advanced Image Processing Techniques (7 papers) and Advanced Image Fusion Techniques (6 papers). Zhenqi Fu collaborates with scholars based in China, South Korea and Singapore. Zhenqi Fu's co-authors include Xinghao Ding, Yue Huang, Yang Yan, Kai‐Kuang Ma, Xiaotong Tu, Feng Shao, Yue Huang, Yo‐Sung Ho, Qiuping Jiang and Liyan Sun and has published in prestigious journals such as IEEE Access, IEEE Transactions on Medical Imaging and IEEE Transactions on Systems Man and Cybernetics Systems.

In The Last Decade

Zhenqi Fu

25 papers receiving 502 citations

Hit Papers

Learning a Simple Low-Light Image Enhancer from Paired Lo... 2023 2026 2024 2025 2023 50 100 150

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhenqi Fu China 12 425 156 31 27 23 27 513
Fangxun Bao China 13 422 1.0× 220 1.4× 39 1.3× 26 1.0× 6 0.3× 24 502
Shuzhou Yang China 8 584 1.4× 197 1.3× 26 0.8× 38 1.4× 39 1.7× 9 649
Wan Nural Jawahir Hj Wan Yussof Malaysia 7 340 0.8× 150 1.0× 16 0.5× 29 1.1× 10 0.4× 23 411
Yali Peng China 10 320 0.8× 132 0.8× 44 1.4× 27 1.0× 5 0.2× 21 419
Zohair Al‐Ameen Iraq 12 403 0.9× 228 1.5× 72 2.3× 45 1.7× 5 0.2× 48 511
Tessamma Thomas India 10 137 0.3× 61 0.4× 49 1.6× 35 1.3× 5 0.2× 62 343
Geet Sahu India 9 262 0.6× 151 1.0× 28 0.9× 22 0.8× 4 0.2× 17 359
Tiantong Guo United States 9 308 0.7× 175 1.1× 21 0.7× 16 0.6× 8 0.3× 13 367
Amit Suveer Sweden 6 312 0.7× 182 1.2× 45 1.5× 42 1.6× 1 0.0× 9 465
Sundaresh Ram United States 11 254 0.6× 56 0.4× 61 2.0× 42 1.6× 1 0.0× 46 431

Countries citing papers authored by Zhenqi Fu

Since Specialization
Citations

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

Fields of papers citing papers by Zhenqi Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenqi Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenqi Fu. A scholar is included among the top collaborators of Zhenqi Fu 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 Zhenqi Fu. Zhenqi Fu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Yan, Ke Zhang, Zhenqi Fu, et al.. (2025). Spatio-Temporal and Retrieval-Augmented Modeling for Chest X-Ray Report Generation. IEEE Transactions on Medical Imaging. 44(7). 2892–2905. 2 indexed citations
3.
Xie, Jiaxin, et al.. (2025). CE$^{3}$USOD: Channel-Enhanced, Efficient, and Effective Network for Underwater Salient Object Detection. IEEE Journal of Oceanic Engineering. 50(2). 941–954. 1 indexed citations
4.
Zhuang, Peixian, et al.. (2025). MCSF-Net: A Multi-Color Space Fusion Network for Underwater Image Enhancement. IEEE Transactions on Multimedia. 28. 1534–1548.
5.
Chen, Sixiang, Zhenqi Fu, Wenhao Chai, et al.. (2025). AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement. Proceedings of the AAAI Conference on Artificial Intelligence. 39(5). 5307–5315. 4 indexed citations
6.
Fu, Zhenqi, Yingying Wang, Cuicui Kong, et al.. (2025). DPLUT: Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors. Proceedings of the AAAI Conference on Artificial Intelligence. 39(5). 5316–5324. 4 indexed citations
7.
Yang, Yan, Jun Yu, Zhenqi Fu, et al.. (2024). Token-Mixer: Bind Image and Text in One Embedding Space for Medical Image Reporting. IEEE Transactions on Medical Imaging. 43(11). 4017–4028. 11 indexed citations
8.
Huang, Jingjia, et al.. (2024). Progressive High-Frequency Reconstruction for Pan-Sharpening with Implicit Neural Representation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(5). 4189–4197. 11 indexed citations
9.
Fu, Zhenqi, et al.. (2023). Domain-irrelevant Feature Learning for Generalizable Pan-sharpening. 3287–3296. 8 indexed citations
10.
Wang, Yingying, et al.. (2023). Learning High-frequency Feature Enhancement and Alignment for Pan-sharpening. 358–367. 11 indexed citations
11.
Fu, Zhenqi, Yang Yan, Xiaotong Tu, et al.. (2023). Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances. 22252–22261. 152 indexed citations breakdown →
12.
Fu, Zhenqi, et al.. (2023). Underwater Image Enhancement and Super-Resolution Using Implicit Neural Networks. 1295–1299. 5 indexed citations
13.
Fu, Zhenqi, et al.. (2023). MASNet: A Robust Deep Marine Animal Segmentation Network. IEEE Journal of Oceanic Engineering. 49(3). 1104–1115. 9 indexed citations
14.
Fu, Zhenqi, et al.. (2022). Underwater Image Enhancement Via Learning Water Type Desensitized Representations. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2764–2768. 34 indexed citations
15.
Shao, Feng, Qiuping Jiang, Zhenqi Fu, et al.. (2021). Combining Retargeting Quality and Depth Perception Measures for Quality Evaluation of Retargeted Stereopairs. IEEE Transactions on Multimedia. 24. 2422–2434. 7 indexed citations
16.
Fu, Zhenqi, Yan Yang, Feng Shao, & Xinghao Ding. (2019). A Study of Perceptual Quality Assessment for Stereoscopic Image Retargeting. 26. 2021–2024. 3 indexed citations
17.
Shao, Feng, Zhenqi Fu, Qiuping Jiang, Gangyi Jiang, & Yo‐Sung Ho. (2019). Transformation-Aware Similarity Measurement for Image Retargeting Quality Assessment via Bidirectional Rewarping. IEEE Transactions on Systems Man and Cybernetics Systems. 51(5). 3053–3067. 20 indexed citations
18.
Yang, Yan, Feng Shao, Zhenqi Fu, & Randi Fu. (2018). Blood vessel segmentation of fundus images via cross-modality dictionary learning. Applied Optics. 57(25). 7287–7287. 5 indexed citations
19.
Fu, Zhenqi, Feng Shao, Qiuping Jiang, Randi Fu, & Yo‐Sung Ho. (2018). Quality Assessment of Retargeted Images Using Hand-Crafted and Deep-Learned Features. IEEE Access. 6. 12008–12018. 18 indexed citations
20.
Wang, Xuming, et al.. (2013). ER-α36-mediated gastric cancer cell proliferation via the c-Src pathway. Oncology Letters. 6(2). 329–335. 28 indexed citations

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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