Jiangxin Dong

2.2k total citations · 1 hit paper
26 papers, 845 citations indexed

About

Jiangxin Dong is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computational Mechanics. According to data from OpenAlex, Jiangxin Dong has authored 26 papers receiving a total of 845 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 14 papers in Media Technology and 3 papers in Computational Mechanics. Recurrent topics in Jiangxin Dong's work include Advanced Image Processing Techniques (20 papers), Image and Signal Denoising Methods (14 papers) and Image Processing Techniques and Applications (12 papers). Jiangxin Dong is often cited by papers focused on Advanced Image Processing Techniques (20 papers), Image and Signal Denoising Methods (14 papers) and Image Processing Techniques and Applications (12 papers). Jiangxin Dong collaborates with scholars based in China, United States and Germany. Jiangxin Dong's co-authors include Jinshan Pan, Jinhui Tang, Ming–Hsuan Yang, Jinshan Pan, Zhixun Su, Bernt Schiele, Stefan Roth, Jianjun Ge, Jimmy Ren and Lingshun Kong and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and International Journal of Computer Vision.

In The Last Decade

Jiangxin Dong

23 papers receiving 825 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.

Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

2023 140 citations Lingshun Kong, Jiangxin Dong et al. profile →

Peers

Jiangxin Dong

CVPR

MT

BE

AI

EEE

Keyu Yan China

Shangchen Zhou Singapore

Naejin Kong South Korea

Yiqun Mei United States

Tetiana Martyniuk Ukraine

Orest Kupyn Ukraine

Zhiying Jiang China

Zhe Hu United States

Long Sun China

Keyu Yan China

Jiangxin Dong

369 ×0.5

CVPR

354 ×0.8

MT

67 ×1.5

BE

40 ×1.1

AI

22 ×0.7

EEE

Citations per year, relative to Jiangxin Dong Jiangxin Dong (= 1×) peers Keyu Yan

Countries citing papers authored by Jiangxin Dong

Since Specialization

Citations

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

Fields of papers citing papers by Jiangxin Dong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiangxin Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Jiangxin Dong. A scholar is included among the top collaborators of Jiangxin Dong 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 Jiangxin Dong. Jiangxin Dong 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:

20 of 20 papers shown

1.

Chen, Xiang, Jinshan Pan, Jiangxin Dong, & Jinhui Tang. (2025). Towards Unified Deep Image Deraining: A Survey and a New Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5414–5433. 5 indexed citations

2.

Pan, Jinshan, et al.. (2025). Learning Efficient Deep Discriminative Spatial and Temporal Networks for Video Deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5708–5722.

3.

Pan, Jinshan, et al.. (2025). FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution. 28188–28197. 2 indexed citations

4.

Kong, Lingshun, Jiangxin Dong, Jinhui Tang, Ming–Hsuan Yang, & Jinshan Pan. (2025). Efficient Visual State Space Model for Image Deblurring. 12710–12719. 1 indexed citations

5.

Sun, Long, et al.. (2025). Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance. 11328–11337.

6.

Wang, Cong, Jinshan Pan, Wanyu Lin, et al.. (2024). SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency. Proceedings of the AAAI Conference on Artificial Intelligence. 38(6). 5327–5335. 21 indexed citations

7.

Chen, Xiang, Jinshan Pan, & Jiangxin Dong. (2024). Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining. 25627–25636. 28 indexed citations

8.

Sun, Long, Jiangxin Dong, Jinhui Tang, & Jinshan Pan. (2023). Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution. 13144–13153. 91 indexed citations

9.

Li, Xiang, Jiangxin Dong, Jinhui Tang, & Jinshan Pan. (2023). DLGSANet: Lightweight Dynamic Local and Global Self-Attention Network for Image Super-Resolution. 12746–12755. 37 indexed citations

10.

Dong, Jiangxin, Haoran Bai, Jinhui Tang, & Jinshan Pan. (2023). Deep Unpaired Blind Image Super-Resolution Using Self-supervised Learning and Exemplar Distillation. International Journal of Computer Vision. 133(11). 8253–8266. 11 indexed citations

11.

Kong, Lingshun, Jiangxin Dong, Jianjun Ge, Mingqiang Li, & Jinshan Pan. (2023). Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring. 5886–5895. 140 indexed citations breakdown →

12.

Dong, Jiangxin, et al.. (2023). PromptRestorer: A Prompting Image Restoration Method with Degradation Perception. 8898–8912.

13.

Pan, Jinshan, et al.. (2023). Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring. 22191–22200. 13 indexed citations

14.

Dong, Jiangxin, Jinshan Pan, Zhong‐Bao Yang, & Jinhui Tang. (2023). Multi-scale Residual Low-Pass Filter Network for Image Deblurring. 12311–12320. 22 indexed citations

15.

Dong, Jiangxin, Stefan Roth, & Bernt Schiele. (2021). Learning Spatially-Variant MAP Models for Non-blind Image Deblurring. 4884–4893. 19 indexed citations

16.

Pan, Jinshan, Jiangxin Dong, Yang Liu, et al.. (2020). Physics-Based Generative Adversarial Models for Image Restoration and Beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(7). 2449–2462. 135 indexed citations

17.

Dong, Jiangxin, Stefan Roth, & Bernt Schiele. (2020). Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring. MPG.PuRe (Max Planck Society). 33. 1048–1059. 36 indexed citations

18.

Pan, Jinshan, Jiangxin Dong, Jimmy Ren, et al.. (2019). Spatially Variant Linear Representation Models for Joint Filtering. 1702–1711. 38 indexed citations

19.

Dong, Jiangxin, Jinshan Pan, Zhixun Su, & Ming–Hsuan Yang. (2017). Blind Image Deblurring with Outlier Handling. 2497–2505. 74 indexed citations

20.

Tang, Kewei, David B. Dunson, Zhixun Su, et al.. (2015). Subspace segmentation by dense block and sparse representation. Neural Networks. 75. 66–76. 21 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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