Deyu Meng
Impact in
- Computational Mathematics top 0.1%
- Media Technology top 0.01%
- Advanced Image Fusion Techniques
- Remote-Sensing Image Classification
Papers in
-
- Image and Signal Denoising Methods 74
- Advanced Image Processing Techniques 46
- Image Enhancement Techniques 29
- Face and Expression Recognition 26
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- Domain Adaptation and Few-Shot Learning 26
- Co-authors
- Wangmeng Zuo (33 shared papers)Lei Zhang (21 shared papers)Kai Zhang (1 shared paper)Yunjin Chen (1 shared paper)Qian Zhao (71 shared papers)Zongben Xu (45 shared papers)Qi Xie (33 shared papers)Chenqiang Gao (26 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (24 papers)IEEE Transactions on Geoscience and Remote Sensing (19 papers)IEEE Transactions on Neural Networks and Learning Systems (18 papers)IEEE Transactions on Image Processing (14 papers)Neurocomputing (13 papers)
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
Deyu Meng
330 papers receiving 22.1k citations
Deyu Meng's Hit Papers
Peers
Comparison fields: 5 of 184
- Computational Mathematics 686
- Media Technology 7.7k
- Computer Vision and Pattern Recognition 14.6k
- Computational Mechanics 2.8k
- Artificial Intelligence 3.4k
Countries citing papers authored by Deyu Meng
This map shows the geographic impact of Deyu Meng'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 Deyu Meng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deyu Meng more than expected).
Fields of papers citing papers by Deyu Meng
This network shows the impact of papers produced by Deyu Meng. 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 Deyu Meng. The network helps show where Deyu Meng may publish in the future.
Co-authors
The 25 scholars most cited alongside Deyu Meng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 343 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising Hit paper breakdown → | 2017 | 5915 |
| 2 | Infrared Patch-Image Model for Small Target Detection in a Single Image Hit paper breakdown → | 2013 | 938 |
| 3 | Progressive Image Deraining Networks: A Better and Simpler Baseline Hit paper breakdown → | 2019 | 689 |
| 4 | Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision Hit paper breakdown → | 2016 | 635 |
| 5 | Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework Hit paper breakdown → | 2016 | 439 |
| 6 | Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition Hit paper breakdown → | 2017 | 359 |
| 7 | Self-Paced Curriculum Learning Hit paper breakdown → | 2015 | 345 |
| 8 | Semi-Supervised Transfer Learning for Image Rain Removal Hit paper breakdown → | 2019 | 339 |
| 9 | A Model-Driven Deep Neural Network for Single Image Rain Removal Hit paper breakdown → | 2020 | 320 |
| 10 | A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding Hit paper breakdown → | 2013 | 307 |
| 11 | Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising Hit paper breakdown → | 2014 | 303 |
| 12 | Hyperspectral Image Classification With Convolutional Neural Network and Active Learning Hit paper breakdown → | 2020 | 291 |
| 13 | 2017 | 269 | |
| 14 | 2015 | 260 | |
| 15 | Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery Hit paper breakdown → | 2017 | 244 |
| 16 | 2019 | 234 | |
| 17 | Self-Paced Learning with Diversity | 2014 | 219 |
| 18 | 2014 | 218 | |
| 19 | 2016 | 212 | |
| 20 | EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network Hit paper breakdown → | 2023 | 202 |
About Deyu Meng
Deyu Meng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Computational Mechanics and Radiology, Nuclear Medicine and Imaging, having authored 343 papers that have together received 22.6k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (74 papers), Advanced Image Fusion Techniques (49 papers), Sparse and Compressive Sensing Techniques (47 papers), Advanced Image Processing Techniques (46 papers), Remote-Sensing Image Classification (45 papers), Image Enhancement Techniques (29 papers), Face and Expression Recognition (26 papers) and Domain Adaptation and Few-Shot Learning (26 papers). The work is most often cited by research in Computational Mathematics (686 citations), Media Technology (7.7k citations), Computer Vision and Pattern Recognition (14.6k citations), Computational Mechanics (2.8k citations) and Artificial Intelligence (3.4k citations). Deyu Meng has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Wangmeng Zuo, Lei Zhang, Kai Zhang, Yunjin Chen, Qian Zhao, Zongben Xu, Qi Xie, Chenqiang Gao, Alexander G. Hauptmann and Yi Yang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Image Processing and Neurocomputing.
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.