Sung‐Ho Bae

2.4k total citations
75 papers, 1.2k citations indexed

About

Sung‐Ho Bae is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Sung‐Ho Bae has authored 75 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 11 papers in Media Technology. Recurrent topics in Sung‐Ho Bae's work include Image and Video Quality Assessment (16 papers), Advanced Image Processing Techniques (14 papers) and Advanced Neural Network Applications (12 papers). Sung‐Ho Bae is often cited by papers focused on Image and Video Quality Assessment (16 papers), Advanced Image Processing Techniques (14 papers) and Advanced Neural Network Applications (12 papers). Sung‐Ho Bae collaborates with scholars based in South Korea, Bangladesh and United Arab Emirates. Sung‐Ho Bae's co-authors include Munchurl Kim, Jiyoung Jung, Myung Jin Kim, Daeyoung Kim, Minkeun Ha, Muhammad Awais, Sungmin Hong, Wooyoung Jung, Jae-Eon Kim and Sang Chan Park and has published in prestigious journals such as The Astrophysical Journal, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Sung‐Ho Bae

61 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sung‐Ho Bae South Korea 19 762 181 176 160 152 75 1.2k
Tomas Akenine‐Möller Sweden 28 1.7k 2.2× 202 1.1× 88 0.5× 82 0.5× 104 0.7× 97 2.3k
K.R. Ramakrishnan India 16 826 1.1× 134 0.7× 154 0.9× 159 1.0× 132 0.9× 85 1.2k
Shuyuan Zhu China 19 1.1k 1.4× 280 1.5× 180 1.0× 127 0.8× 47 0.3× 124 1.3k
Yanghai Tsin United States 13 1.4k 1.8× 51 0.3× 150 0.9× 183 1.1× 47 0.3× 26 1.5k
Maher Jridi France 13 647 0.8× 276 1.5× 81 0.5× 104 0.7× 40 0.3× 43 893
Xin Du China 13 739 1.0× 218 1.2× 372 2.1× 422 2.6× 422 2.8× 45 1.4k
Morgan McGuire United States 20 946 1.2× 49 0.3× 160 0.9× 37 0.2× 76 0.5× 48 1.4k
Jan‐Olof Eklundh Sweden 21 955 1.3× 56 0.3× 194 1.1× 148 0.9× 58 0.4× 70 1.3k
Feng Dai China 17 1.4k 1.8× 467 2.6× 238 1.4× 249 1.6× 61 0.4× 60 1.8k
Kim–Hui Yap Singapore 22 1.3k 1.6× 116 0.6× 329 1.9× 186 1.2× 36 0.2× 138 1.6k

Countries citing papers authored by Sung‐Ho Bae

Since Specialization
Citations

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

Fields of papers citing papers by Sung‐Ho Bae

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sung‐Ho Bae. 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 Sung‐Ho Bae. The network helps show where Sung‐Ho Bae may publish in the future.

Co-authorship network of co-authors of Sung‐Ho Bae

This figure shows the co-authorship network connecting the top 25 collaborators of Sung‐Ho Bae. A scholar is included among the top collaborators of Sung‐Ho Bae 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 Sung‐Ho Bae. Sung‐Ho Bae is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bae, Sung‐Ho, et al.. (2025). Training Deep Learning Segmentation Models Using Super-Resolution Crack Images for Detection of Thin Concrete Cracks. Journal of Computing in Civil Engineering. 39(4). 2 indexed citations
2.
Bae, Sung‐Ho, et al.. (2025). Effects of mixed sample data augmentation on interpretability of neural networks. Neural Networks. 190. 107611–107611.
3.
Kim, Soo Ye, et al.. (2024). Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model. Proceedings of the AAAI Conference on Artificial Intelligence. 38(2). 954–963. 1 indexed citations
4.
5.
Awais, Muhammad, et al.. (2024). Give me a hint: an explicit prior based image denoising. Signal Image and Video Processing. 18(12). 9451–9463. 1 indexed citations
6.
Jeong, Seyoon, et al.. (2023). A Super-Resolution-Based Feature Map Compression for Machine-Oriented Video Coding. IEEE Access. 11. 34198–34209. 2 indexed citations
7.
Muneeb, Muhammad, et al.. (2023). Saliency Prediction in Uncategorized Videos Based on Audio-Visual Correlation. IEEE Access. 11. 15460–15470. 1 indexed citations
8.
Moon, Yong‐Jae, et al.. (2023). Fast Reconstruction of 3D Density Distribution around the Sun Based on the MAS by Deep Learning. The Astrophysical Journal. 948(1). 21–21. 5 indexed citations
9.
Kumar, Teerath, et al.. (2021). Binary-Classifiers-Enabled Filters for Semi-Supervised Learning. IEEE Access. 9. 167663–167673. 18 indexed citations
10.
Chung, TaeChoong, et al.. (2021). SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization. International Conference on Learning Representations. 4 indexed citations
11.
Shin, Gyungin, et al.. (2020). Generation of High-resolution Solar Pseudo-magnetograms from Ca ii K Images by Deep Learning. The Astrophysical Journal Letters. 895(1). L16–L16. 25 indexed citations
12.
13.
Bae, Sung‐Ho, et al.. (2020). Global Weight: Network Level Weight Sharing for Compression of Deep Neural Network. 22–25. 1 indexed citations
14.
Hua, Cam-Hao, Thien Huynh‐The, Kiyoung Kim, et al.. (2019). Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification. International Journal of Medical Informatics. 132. 103926–103926. 42 indexed citations
15.
Bae, Sung‐Ho, et al.. (2015). An HEVC-Compliant Perceptual Video Coding Scheme Based on JND Models for Variable Block-Sized Transform Kernels. IEEE Transactions on Circuits and Systems for Video Technology. 25(11). 1786–1800. 51 indexed citations
16.
Bae, Sung‐Ho, et al.. (2013). An Objective No-Reference Perceptual Quality Assessment Metric based on Temporal Complexity and Disparity for Stereoscopic Video. IEIE Transactions on Smart Processing and Computing. 2(5). 255–265. 1 indexed citations
17.
Bae, Sung‐Ho, et al.. (2013). A NEW DCT-BASED JND MODEL OF MONOCHROME IMAGES FOR CONTRAST MASKING EFFECTS WITH TEXTURE COMPLEXITY AND FREQUENCY. International Conference on Image Processing. 1(1). 431–434. 18 indexed citations
18.
Bae, Sung‐Ho, et al.. (2013). A Study of a Module of Wrist Direction Recognition using EMG Signals. 7(1). 51–58. 5 indexed citations
19.
Bae, Sung‐Ho. (2010). A High Capacity Reversible Watermarking Using Histogram Shifting. Journal of Korea Multimedia Society. 13(1). 76–82. 1 indexed citations
20.
Bae, Sung‐Ho, et al.. (2010). Development of Game Input Device Using Bio-signal. 107–112. 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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