Seungjun Nah
Impact in
- Media Technology top 0.5%
- Image Processing Techniques and Applications
- Advanced Image Fusion Techniques
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- Advanced Image Processing Techniques
- Image and Signal Denoising Methods
- Advanced Vision and Imaging
- Image Enhancement Techniques
- Digital Media Forensic Detection
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Advanced Image Processing Techniques 5
- Advanced Vision and Imaging 4
- Image and Signal Denoising Methods 3
- Image and Video Quality Assessment 1
- Visual Attention and Saliency Detection 1
- Digital Media Forensic Detection 1
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- Image Processing Techniques and Applications 2
- Co-authors
- Kyoung Mu Lee (6 shared papers)Tae Hyun Kim (1 shared paper)Sanghyun Son (2 shared papers)Gyeongsik Moon (1 shared paper)Sungyong Baik (1 shared paper)Seokil Hong (1 shared paper)Radu Timofte (1 shared paper)Jia‐Bin Huang (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- South KoreaUnited StatesSwitzerland
In The Last Decade
Seungjun Nah
7 papers receiving 1.8k citations
Seungjun Nah's Hit Papers
Peers
Comparison fields: 5 of 73
- Media Technology 764
- Computer Vision and Pattern Recognition 1.7k
- Acoustics and Ultrasonics 22
- Computer Graphics and Computer-Aided Design 34
- Instrumentation 22
Countries citing papers authored by Seungjun Nah
This map shows the geographic impact of Seungjun Nah'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 Seungjun Nah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seungjun Nah more than expected).
Fields of papers citing papers by Seungjun Nah
This network shows the impact of papers produced by Seungjun Nah. 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 Seungjun Nah. The network helps show where Seungjun Nah may publish in the future.
Co-authors
The 25 scholars most cited alongside Seungjun Nah, 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 | Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring Hit paper breakdown → | 2017 | 1354 |
| 2 | 2019 | 278 | |
| 3 | 2019 | 85 | |
| 4 | 2023 | 60 | |
| 5 | 2017 | 39 | |
| 6 | 2022 | 10 | |
| 7 | 2015 | 1 | |
| 8 | 2024 | 0 |
About Seungjun Nah
Seungjun Nah is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Sensory Systems, Infectious Diseases and Organic Chemistry, having authored 8 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (4 papers), Image and Signal Denoising Methods (3 papers), Image Processing Techniques and Applications (2 papers), Image and Video Quality Assessment (1 paper), Visual Attention and Saliency Detection (1 paper), Olfactory and Sensory Function Studies (1 paper) and Digital Media Forensic Detection (1 paper). The work is most often cited by research in Media Technology (764 citations), Computer Vision and Pattern Recognition (1.7k citations), Acoustics and Ultrasonics (22 citations), Computer Graphics and Computer-Aided Design (34 citations) and Instrumentation (22 citations). Seungjun Nah has collaborated with scholars based in South Korea, United States and Switzerland. Frequent co-authors include Kyoung Mu Lee, Tae Hyun Kim, Sanghyun Son, Gyeongsik Moon, Sungyong Baik, Seokil Hong, Radu Timofte, Tae Hyun Kim, Jia‐Bin Huang and Songwei Ge. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.