Shaolin Su

901 total citations · 1 hit paper
12 papers, 518 citations indexed

About

Shaolin Su is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computer Networks and Communications. According to data from OpenAlex, Shaolin Su has authored 12 papers receiving a total of 518 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 6 papers in Media Technology and 1 paper in Computer Networks and Communications. Recurrent topics in Shaolin Su's work include Image and Video Quality Assessment (6 papers), Advanced Image Fusion Techniques (5 papers) and Advanced Image Processing Techniques (5 papers). Shaolin Su is often cited by papers focused on Image and Video Quality Assessment (6 papers), Advanced Image Fusion Techniques (5 papers) and Advanced Image Processing Techniques (5 papers). Shaolin Su collaborates with scholars based in China, Germany and Australia. Shaolin Su's co-authors include Jinqiu Sun, Yanning Zhang, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Vlad Hosu, Yu Zhu, Qing Mao and Yu Zhu and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Multimedia and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Shaolin Su

12 papers receiving 501 citations

Hit Papers

align trajectories

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.

Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network

2020 451 citations Shaolin Su, Qingsen Yan et al. Adelaide Research & Scholarship (AR&S) (University of Adelaide) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaolin Su China	5	446	236	31	23	19	12	518
S. Alireza Golestaneh United States	6	432 1.0×	287 1.2×	27 0.9×	22 1.0×	26 1.4×	10	483
Haoning Wu Singapore	12	397 0.9×	140 0.6×	24 0.8×	27 1.2×	4 0.2×	38	533
Jingtao Xu China	8	596 1.3×	370 1.6×	17 0.5×	11 0.5×	37 1.9×	12	635
Seo-Won Ji South Korea	6	447 1.0×	221 0.9×	8 0.3×	16 0.7×	11 0.6×	12	502
Tetiana Martyniuk Ukraine	2	606 1.4×	291 1.2×	10 0.3×	29 1.3×	11 0.6×	5	666
Orest Kupyn Ukraine	2	606 1.4×	291 1.2×	10 0.3×	29 1.3×	11 0.6×	2	666
Balu Adsumilli United States	12	497 1.1×	137 0.6×	14 0.5×	9 0.4×	18 0.9×	38	546
Shuochen Su Canada	7	508 1.1×	235 1.0×	11 0.4×	16 0.7×	29 1.5×	9	613
Saba Dadsetan United States	4	190 0.4×	127 0.5×	21 0.7×	38 1.7×	9 0.5×	6	260
Hossein Ziaei Nafchi Canada	9	286 0.6×	163 0.7×	11 0.4×	25 1.1×	32 1.7×	13	349

Countries citing papers authored by Shaolin Su

Since Specialization

Citations

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

Fields of papers citing papers by Shaolin Su

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaolin Su

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

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Su, Shaolin, et al.. (2025). DT-RSRGAN: An one-off domain translation generative model for real image super-resolution. Pattern Recognition. 169. 111944–111944. 1 indexed citations

2.

Jenadeleh, Mohsen, Shaolin Su, João Ascenso, et al.. (2025). Fine-Grained Subjective Visual Quality Assessment for High-Fidelity Compressed Images. 123–132. 4 indexed citations

3.

Su, Shaolin, et al.. (2024). GSDD: Generative Space Dataset Distillation for Image Super-resolution. Proceedings of the AAAI Conference on Artificial Intelligence. 38(7). 7069–7077. 1 indexed citations

4.

Hosu, Vlad, et al.. (2023). Konx: cross-resolution image quality assessment. KOPS (University of Konstanz). 8(1). 3 indexed citations

5.

Su, Shaolin, et al.. (2023). Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance. Pattern Recognition. 137. 109297–109297. 20 indexed citations

6.

Su, Shaolin, et al.. (2023). Boosting No-Reference Super-Resolution Image Quality Assessment with Knowledge Distillation and Extension. 1–5. 4 indexed citations

7.

Su, Shaolin, Hanhe Lin, Vlad Hosu, et al.. (2023). Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model. IEEE Transactions on Multimedia. 26. 2671–2685. 11 indexed citations

8.

Zhang, Cheng, Shaolin Su, Yu Zhu, et al.. (2022). Exploring and Evaluating Image Restoration Potential in Dynamic Scenes. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2057–2066. 5 indexed citations

9.

Su, Shaolin, Qingsen Yan, Yu Zhu, Jinqiu Sun, & Yanning Zhang. (2022). From Distortion Manifold to Perceptual Quality: a Data Efficient Blind Image Quality Assessment Approach. Pattern Recognition. 133. 109047–109047. 14 indexed citations

10.

Su, Shaolin, Vlad Hosu, Hanhe Lin, Yanning Zhang, & Dietmar Saupe. (2021). KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects. 3 indexed citations

11.

Su, Shaolin, Qingsen Yan, Yu Zhu, et al.. (2020). Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 3664–3673. 451 indexed citations breakdown →

12.

Su, Shaolin, Lin Ma, & Yubin Xu. (2015). A Fast Radio Map Building Method Based on Floor Plan and Accelerometer. 5. 392–396. 1 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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