Seungjun Nah

5.7k total citations · 1 hit paper
8 papers, 1.8k citations indexed

About

Seungjun Nah is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Sensory Systems. According to data from OpenAlex, Seungjun Nah has authored 8 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 2 papers in Media Technology and 1 paper in Sensory Systems. Recurrent topics in Seungjun Nah's work include Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (4 papers) and Image and Signal Denoising Methods (3 papers). Seungjun Nah is often cited by papers focused on Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (4 papers) and Image and Signal Denoising Methods (3 papers). Seungjun Nah collaborates with scholars based in South Korea, United States and Switzerland. Seungjun Nah's co-authors include Kyoung Mu Lee, Tae Hyun Kim, Sanghyun Son, Radu Timofte, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Tae Hyun Kim, Andrew Tao and Bryan Catanzaro and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Seungjun Nah

7 papers receiving 1.8k 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.

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

2017 1.4k citations Seungjun Nah, Tae Hyun Kim et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seungjun Nah South Korea	6	1.7k	764	70	65	61	8	1.8k
Jiawei Zhang China	17	1.5k 0.9×	739 1.0×	54 0.8×	68 1.0×	34 0.6×	41	1.7k
Jianrui Cai Hong Kong	9	2.4k 1.4×	1.4k 1.8×	112 1.6×	76 1.2×	56 0.9×	9	2.6k
Yuanchao Bai China	12	1.2k 0.7×	585 0.8×	46 0.7×	80 1.2×	36 0.6×	26	1.4k
Muhammad Haris Japan	8	1.5k 0.9×	869 1.1×	52 0.7×	29 0.4×	54 0.9×	18	1.6k
Xiangyu Xu China	14	1.3k 0.8×	497 0.7×	58 0.8×	49 0.8×	30 0.5×	33	1.5k
Jinqiu Sun China	17	1.1k 0.6×	462 0.6×	60 0.9×	73 1.1×	65 1.1×	73	1.3k
Fan‐Chieh Cheng Taiwan	16	1.3k 0.8×	593 0.8×	80 1.1×	73 1.1×	71 1.2×	35	1.5k
Kin Gwn Lore United States	12	1.2k 0.7×	523 0.7×	118 1.7×	88 1.4×	69 1.1×	19	1.5k
Yiping Deng Sweden	3	1.2k 0.7×	545 0.7×	80 1.1×	228 3.5×	77 1.3×	3	1.4k

Countries citing papers authored by Seungjun Nah

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Seungjun Nah

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

8 of 8 papers shown

1.

Lin, Tsung-Yi, Yin Cui, Yunhao Ge, et al.. (2024). GenUSD: 3D scene generation made easy. 1–2.

2.

Ge, Songwei, Seungjun Nah, Guilin Liu, et al.. (2023). Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models. 22873–22884. 60 indexed citations

3.

Kim, Heewon, et al.. (2022). Attentive Fine-Grained Structured Sparsity for Image Restoration. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 17652–17661. 10 indexed citations

4.

Nah, Seungjun, Sungyong Baik, Seokil Hong, et al.. (2019). NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study. 1996–2005. 278 indexed citations

5.

Nah, Seungjun, Sanghyun Son, & Kyoung Mu Lee. (2019). Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring. 8094–8103. 85 indexed citations

6.

Kim, Tae Hyun, Seungjun Nah, & Kyoung Mu Lee. (2017). Dynamic Video Deblurring Using a Locally Adaptive Blur Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(10). 2374–2387. 39 indexed citations

7.

Nah, Seungjun, Tae Hyun Kim, & Kyoung Mu Lee. (2017). Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring. 257–265. 1354 indexed citations breakdown →

8.

Nah, Seungjun & Kyoung Mu Lee. (2015). Random forest with data ensemble for saliency detection. 20. 604–607. 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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