Junru Wu
Impact in
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- Advanced Image Processing Techniques
- Image and Signal Denoising Methods
- Visual Attention and Saliency Detection
- Image and Video Quality Assessment
- Advanced Vision and Imaging
- Image Enhancement Techniques
- Media Technology top 1%
- Image Processing Techniques and Applications
- Advanced Image Fusion Techniques
Papers in
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- Visual Attention and Saliency Detection 3
- Advanced Image Processing Techniques 2
- Digital Media Forensic Detection 2
- Image and Video Quality Assessment 1
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- Natural Language Processing Techniques 2
- Topic Modeling 2
- Co-authors
- Zhangyang Wang (2 shared papers)Orest Kupyn (1 shared paper)Tetiana Martyniuk (1 shared paper)Yanyu Xu (3 shared papers)Jingyi Yu (3 shared papers)Shenghua Gao (3 shared papers)Zhiru Shi (1 shared paper)Nianyi Li (2 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)The HKU Scholars Hub (University of Hong Kong) (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesChinaUkraine
In The Last Decade
Junru Wu
8 papers receiving 936 citations
Hit Papers
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 833
- Media Technology 309
- Human-Computer Interaction 76
- Acoustics and Ultrasonics 7
- Signal Processing 63
Countries citing papers authored by Junru Wu
This map shows the geographic impact of Junru Wu'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 Junru Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junru Wu more than expected).
Fields of papers citing papers by Junru Wu
This network shows the impact of papers produced by Junru Wu. 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 Junru Wu. The network helps show where Junru Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Junru Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better Hit paper breakdown → | 2019 | 639 |
| 2 | 2018 | 189 | |
| 3 | 2024 | 46 | |
| 4 | 2018 | 35 | |
| 5 | 2017 | 17 | |
| 6 | 2020 | 17 | |
| 7 | 2024 | 11 | |
| 8 | Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions | 2018 | 6 |
| 9 | 2025 | 1 | |
| 10 | 2025 | 0 |
About Junru Wu
Junru Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Urban Studies and Cognitive Neuroscience, having authored 10 papers that have together received 961 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (3 papers), Visual Attention and Saliency Detection (3 papers), Natural Language Processing Techniques (2 papers), Advanced Image Processing Techniques (2 papers), Topic Modeling (2 papers), Digital Media Forensic Detection (2 papers), Image and Video Quality Assessment (1 paper) and Data Management and Algorithms (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (833 citations), Media Technology (309 citations), Human-Computer Interaction (76 citations), Acoustics and Ultrasonics (7 citations) and Signal Processing (63 citations). Junru Wu has collaborated with scholars based in United States, China and Ukraine. Frequent co-authors include Zhangyang Wang, Orest Kupyn, Tetiana Martyniuk, Yanyu Xu, Jingyi Yu, Shenghua Gao, Zhiru Shi, Nianyi Li, Kai Hui and Michael Bendersky. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The HKU Scholars Hub (University of Hong Kong) and International Conference on Machine Learning.
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.