Mai Xu

8.6k total citations · 1 hit paper
214 papers, 5.6k citations indexed

About

Mai Xu is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology. According to data from OpenAlex, Mai Xu has authored 214 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 158 papers in Computer Vision and Pattern Recognition, 46 papers in Signal Processing and 29 papers in Media Technology. Recurrent topics in Mai Xu's work include Image and Video Quality Assessment (58 papers), Advanced Image Processing Techniques (54 papers) and Advanced Vision and Imaging (45 papers). Mai Xu is often cited by papers focused on Image and Video Quality Assessment (58 papers), Advanced Image Processing Techniques (54 papers) and Advanced Vision and Imaging (45 papers). Mai Xu collaborates with scholars based in China, United Kingdom and United States. Mai Xu's co-authors include Zulin Wang, Xin Deng, Ren Yang, Lai Jiang, Zhenyu Guan, Chen Li, F.C. Lee, Shengxi Li, Tianyi Li and Xiaofei Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Power Electronics and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Mai Xu

197 papers receiving 5.4k citations

Hit Papers

Domain Adaptation for Underwater Image Enhancement 2023 2026 2024 2025 2023 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Domain Adaptation for Underwater Image Enhancement
    2023 115 citations Mai Xu et al. IEEE Transactions on Image Processing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mai Xu China 40 3.5k 1.2k 857 689 674 214 5.6k
Xinge You China 34 2.6k 0.8× 368 0.3× 513 0.6× 566 0.8× 707 1.0× 148 4.2k
Herbert Bay Switzerland 6 7.6k 2.2× 353 0.3× 593 0.7× 308 0.4× 1.0k 1.6× 7 9.4k
Zulin Wang China 27 1.8k 0.5× 761 0.7× 488 0.6× 289 0.4× 286 0.4× 151 3.0k
Andreas Ess Switzerland 13 9.6k 2.8× 429 0.4× 715 0.8× 288 0.4× 1.1k 1.6× 19 11.7k
Hang Zhao United States 20 4.0k 1.1× 690 0.6× 651 0.8× 409 0.6× 841 1.2× 50 6.6k
Mingkui Tan China 40 5.4k 1.6× 361 0.3× 350 0.4× 473 0.7× 917 1.4× 151 8.1k
In So Kweon South Korea 44 7.9k 2.3× 338 0.3× 480 0.6× 322 0.5× 1.9k 2.8× 321 9.9k
Clément Farabet United States 12 2.7k 0.8× 238 0.2× 888 1.0× 304 0.4× 427 0.6× 16 4.8k
Vincent Lepetit Switzerland 47 9.8k 2.8× 238 0.2× 642 0.7× 269 0.4× 702 1.0× 151 11.9k

Countries citing papers authored by Mai Xu

Since Specialization
Citations

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

Fields of papers citing papers by Mai Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mai Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Mai Xu. A scholar is included among the top collaborators of Mai Xu 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 Mai Xu. Mai Xu 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.
Li, Shengxi, et al.. (2025). Hierarchical Semantic Compression for Consistent Image Semantic Restoration. IEEE Transactions on Image Processing. 34. 6767–6782.
2.
Li, Shengxi, et al.. (2025). Continuous Patch Stitching for Block-Wise Image Compression. IEEE Signal Processing Letters. 32. 2699–2703. 1 indexed citations
3.
Xu, Mai, et al.. (2025). MVL-Net: Pairwise Learning for Multi-View Multiple People Labelling. IEEE Transactions on Multimedia. 27. 4736–4751.
4.
Xu, Mai, Z. H. Lu, Gongpu Lan, et al.. (2024). An Efficient OCT Fingerprint Antispoofing Method Based on ResMamba. Symmetry. 16(12). 1603–1603.
5.
Xu, Jingyi, et al.. (2024). Laplacian Gradient Consistency Prior for Flash Guided Non-Flash Image Denoising. IEEE Transactions on Image Processing. 33. 6380–6392. 3 indexed citations
6.
Wang, Miaohui, et al.. (2024). Blind Multimodal Quality Assessment of Low-Light Images. International Journal of Computer Vision. 133(4). 1665–1688. 2 indexed citations
7.
Jiang, Lai, et al.. (2023). Deep multi-task learning for nephropathy diagnosis on immunofluorescence images. Computer Methods and Programs in Biomedicine. 241. 107747–107747.
8.
Lian, Liping, Mai Xu, Weiguo Song, et al.. (2022). Characteristics of merging behavior in large crowds. Journal of Statistical Mechanics Theory and Experiment. 2022(1). 13403–13403. 9 indexed citations
9.
Guan, Zhenyu, et al.. (2022). DeepMIH: Deep Invertible Network for Multiple Image Hiding. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(1). 372–390. 131 indexed citations
10.
Yang, Li, et al.. (2021). Hierarchical Bayesian LSTM for Head Trajectory Prediction on Omnidirectional Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 7563–7580. 15 indexed citations
11.
Li, Tianyi, et al.. (2021). DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC. IEEE Transactions on Image Processing. 30. 5377–5390. 128 indexed citations
12.
Xu, Yongli, Man Hu, Hanruo Liu, et al.. (2021). A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis. npj Digital Medicine. 4(1). 48–48. 33 indexed citations
13.
Liu, Tie, et al.. (2021). MRS-Net+ for Enhancing Face Quality of Compressed Videos. IEEE Transactions on Circuits and Systems for Video Technology. 32(5). 2881–2894. 5 indexed citations
14.
Li, Li, et al.. (2020). Using Minimum Component and CNN for Satellite Remote Sensing Image Cloud Detection. IEEE Geoscience and Remote Sensing Letters. 18(12). 2162–2166. 11 indexed citations
15.
Guan, Zhenyu, Qunliang Xing, Mai Xu, et al.. (2019). MFQE 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(3). 949–963. 177 indexed citations
16.
Ren, Jie, Zulin Wang, Mai Xu, Fang Fang, & Zhiguo Ding. (2019). An EM-Based User Clustering Method in Non-Orthogonal Multiple Access. IEEE Transactions on Communications. 67(12). 8422–8434. 24 indexed citations
17.
Xu, Mai, et al.. (2018). Fast H.264 to HEVC Transcoding: A Deep Learning Method. IEEE Transactions on Multimedia. 21(7). 1633–1645. 14 indexed citations
18.
Ding, Zhiguo, Mai Xu, Yan Chen, Mugen Peng, & H. Vincent Poor. (2018). Embracing non-orthogonalmultiple access in future wireless networks. Frontiers of Information Technology & Electronic Engineering. 19(3). 322–339. 22 indexed citations
19.
Xu, Mai, et al.. (2018). Understanding and Predicting the Memorability of Natural Scene Images.. arXiv (Cornell University). 1 indexed citations
20.
Ren, Jie, Mai Xu, Wei Chen, Zhiguo Ding, & Zulin Wang. (2017). Coalition Formation Approaches for Cooperative Networks With SWIPT. IEEE Access. 5. 17644–17659. 5 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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