Chang‐Hwan Son

763 total citations
72 papers, 515 citations indexed

About

Chang‐Hwan Son is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Media Technology. According to data from OpenAlex, Chang‐Hwan Son has authored 72 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 23 papers in Atomic and Molecular Physics, and Optics and 23 papers in Media Technology. Recurrent topics in Chang‐Hwan Son's work include Image Enhancement Techniques (34 papers), Color Science and Applications (23 papers) and Advanced Image Fusion Techniques (16 papers). Chang‐Hwan Son is often cited by papers focused on Image Enhancement Techniques (34 papers), Color Science and Applications (23 papers) and Advanced Image Fusion Techniques (16 papers). Chang‐Hwan Son collaborates with scholars based in South Korea, Canada and Germany. Chang‐Hwan Son's co-authors include Xiao–Ping Zhang, Hyunseung Choo, Yeong‐Ho Ha, Hyung‐Min Park, Dong Hyuk Lee, Cheol‐Hee Lee, Jong-Man Kim, Jongman Kim, Hyeonjoon Moon and Prasanta K. Sahoo and has published in prestigious journals such as IEEE Transactions on Image Processing, Optics Letters and IEEE Access.

In The Last Decade

Chang‐Hwan Son

58 papers receiving 460 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chang‐Hwan Son South Korea 12 376 189 144 95 50 72 515
David Connah United Kingdom 11 256 0.7× 109 0.6× 282 2.0× 8 0.1× 49 1.0× 30 483
Steven D. Hordley United Kingdom 9 517 1.4× 128 0.7× 413 2.9× 8 0.1× 21 0.4× 17 630
H.M.G. Stokman Netherlands 9 293 0.8× 113 0.6× 81 0.6× 13 0.1× 12 0.2× 20 373
Samia Aïnouz France 11 160 0.4× 49 0.3× 24 0.2× 33 0.3× 100 2.0× 31 287
Jean-Christophe Terrillon Japan 6 410 1.1× 48 0.3× 55 0.4× 9 0.1× 20 0.4× 14 507
Michela Lecca Italy 13 307 0.8× 89 0.5× 70 0.5× 8 0.1× 18 0.4× 52 412
Damien Muselet France 10 276 0.7× 76 0.4× 44 0.3× 8 0.1× 10 0.2× 34 358
Henry R. Kang United States 7 299 0.8× 59 0.3× 322 2.2× 6 0.1× 13 0.3× 13 484
K. Sobottka Switzerland 8 561 1.5× 68 0.4× 30 0.2× 7 0.1× 21 0.4× 15 646
R.E.N. Horne United Kingdom 4 177 0.5× 61 0.3× 27 0.2× 11 0.1× 27 0.5× 7 273

Countries citing papers authored by Chang‐Hwan Son

Since Specialization
Citations

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

Fields of papers citing papers by Chang‐Hwan Son

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang‐Hwan Son. 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 Chang‐Hwan Son. The network helps show where Chang‐Hwan Son may publish in the future.

Co-authorship network of co-authors of Chang‐Hwan Son

This figure shows the co-authorship network connecting the top 25 collaborators of Chang‐Hwan Son. A scholar is included among the top collaborators of Chang‐Hwan Son 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 Chang‐Hwan Son. Chang‐Hwan Son 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.
Son, Chang‐Hwan. (2025). Locally Grouped and Scale‐Guided Attention for Dense Pest Counting. International Journal of Intelligent Systems. 2025(1). 1 indexed citations
2.
Son, Chang‐Hwan, et al.. (2025). ROI-aware multiscale cross-attention vision transformer for pest image identification. Computers and Electronics in Agriculture. 237. 110732–110732.
3.
Son, Chang‐Hwan, et al.. (2023). Multiscale and Deformable Density Attention Model for Trap-based Pest Counting. The Journal of Korean Institute of Information Technology. 21(12). 1–11. 2 indexed citations
4.
Joshi, Gyanendra Prasad, et al.. (2023). Cuffless Blood Pressure Estimation with Confidence Intervals using Hybrid Feature Selection and Decision Based on Gaussian Process. Applied Sciences. 13(2). 1221–1221. 2 indexed citations
5.
Joshi, Gyanendra Prasad, et al.. (2023). Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation. Diagnostics. 13(4). 736–736. 2 indexed citations
6.
Son, Chang‐Hwan, et al.. (2023). Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning. Remote Sensing. 15(15). 3810–3810. 4 indexed citations
7.
Son, Chang‐Hwan, et al.. (2022). Pest Image Superresolution using Class-Specific Perceptual Loss Model and Region-of-Interests Features. The Journal of Korean Institute of Information Technology. 20(8). 123–132.
8.
Moon, Hyeonjoon, et al.. (2022). Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm. Applied Sciences. 12(16). 8355–8355. 7 indexed citations
9.
Son, Chang‐Hwan, et al.. (2021). New Encoder Learning for Captioning Heavy Rain Images via Semantic Visual Feature Matching. Journal of Imaging Science and Technology. 65(5). 50402–1. 3 indexed citations
10.
Son, Chang‐Hwan, et al.. (2021). Image Captioning via Semantic Visual Feature Matching in Heavy Rain Condition. The Journal of Korean Institute of Information Technology. 19(5). 19–29. 1 indexed citations
11.
Son, Chang‐Hwan, et al.. (2020). Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble Gradient Boosting in Estimating Respiratory Rate. IEEE Access. 8. 125648–125658. 5 indexed citations
12.
Son, Chang‐Hwan, et al.. (2019). Rain Removal Via Deep Convolutional Neural Networks Considering Orientation and Strength of Rain Streaks. The Journal of Korean Institute of Information Technology. 17(1). 85–98. 2 indexed citations
13.
Son, Chang‐Hwan. (2017). Rain Detection via Deep Convolutional Neural Networks. Journal of the Institute of Electronics Engineers of Korea. 54(8). 81–88. 1 indexed citations
14.
Son, Chang‐Hwan & Xiao–Ping Zhang. (2017). Multimodal fusion via a series of transfers for noise removal. 13. 530–534. 1 indexed citations
15.
Son, Chang‐Hwan, et al.. (2014). Inverse color to black-and-white halftone conversion via dictionary learning and color mapping. Information Sciences. 299. 1–19. 7 indexed citations
16.
Son, Chang‐Hwan & Hyunseung Choo. (2014). Watermark detection from clustered halftone dots via learned dictionary. Signal Processing. 102. 77–84. 11 indexed citations
17.
Son, Chang‐Hwan. (2012). Inverse halftoning based on sparse representation. Optics Letters. 37(12). 2352–2352. 14 indexed citations
18.
Son, Chang‐Hwan, Cheol‐Hee Lee, & Yeong‐Ho Ha. (2007). Color Correction of a Projected Image on Colored-Screen for Beam-Projector. Journal of the Institute of Electronics Engineers of Korea. 44(4). 35–43. 1 indexed citations
19.
Son, Chang‐Hwan, Cheol‐Hee Lee, Kil-Houm Park, & Yeong‐Ho Ha. (2007). Real-Time Color Matching Between Camera and LCD Based on 16-bit Lookup Table Design in Mobile Phone. Journal of Imaging Science and Technology. 51(4). 348–359. 3 indexed citations
20.
Son, Chang‐Hwan, et al.. (2006). Implementation of a real-time color matching between mobile camera and mobile LCD based on 16-bit LUT design. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6392. 63920R–63920R. 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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