Eunju Cha

514 total citations
10 papers, 306 citations indexed

About

Eunju Cha is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Structural Biology. According to data from OpenAlex, Eunju Cha has authored 10 papers receiving a total of 306 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Biomedical Engineering and 3 papers in Structural Biology. Recurrent topics in Eunju Cha's work include Advanced Electron Microscopy Techniques and Applications (3 papers), Medical Imaging Techniques and Applications (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Eunju Cha is often cited by papers focused on Advanced Electron Microscopy Techniques and Applications (3 papers), Medical Imaging Techniques and Applications (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Eunju Cha collaborates with scholars based in South Korea and United States. Eunju Cha's co-authors include Jong Chul Ye, Yoseob Han, Hyungjin Chung, Junho Lee, Eunha Lee, Jaeduck Jang, Myoungho Jeong, Shinae Jun, Sungwoo Hwang and Eung Yeop Kim and has published in prestigious journals such as ACS Nano, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Eunju Cha

8 papers receiving 301 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eunju Cha South Korea	7	147	104	104	41	33	10	306
Jonas Adler Sweden	6	184 1.3×	72 0.7×	232 2.2×	23 0.6×	10 0.3×	14	367
Lucio Azzari Finland	10	68 0.5×	184 1.8×	78 0.8×	34 0.8×	7 0.2×	13	328
E.U. Mumcuoglu Türkiye	12	534 3.6×	63 0.6×	247 2.4×	40 1.0×	16 0.5×	26	711
Rodney Shaw United States	8	202 1.4×	43 0.4×	236 2.3×	6 0.1×	28 0.8×	30	440
William Scullin United States	6	72 0.5×	14 0.1×	57 0.5×	6 0.1×	41 1.2×	13	209
S. Vázquez-Montiel Mexico	8	52 0.4×	56 0.5×	140 1.3×	32 0.8×	7 0.2×	78	303
Jiancheng Lai China	12	66 0.4×	133 1.3×	208 2.0×	32 0.8×	43 1.3×	64	494
Jürgen Frikel Germany	9	222 1.5×	71 0.7×	231 2.2×	43 1.0×	2 0.1×	17	331
Ingwer C. Carlsen Germany	7	135 0.9×	70 0.7×	19 0.2×	14 0.3×	9 0.3×	18	250

Countries citing papers authored by Eunju Cha

Since Specialization

Citations

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

Fields of papers citing papers by Eunju Cha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eunju Cha. 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 Eunju Cha. The network helps show where Eunju Cha may publish in the future.

Co-authorship network of co-authors of Eunju Cha

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

10 of 10 papers shown

1.

Yoo, Sungjoo, et al.. (2025). Haar Wavelet-Based Representation Learning for Unpaired Image-to-Image Translation. IEEE Access. 13. 61821–61832.

2.

Cha, Eunju. (2024). Regularization by denoising diffusion process meets deep relaxation in phase. Image and Vision Computing. 151. 105282–105282. 1 indexed citations

3.

Song, Seung Hyun, et al.. (2024). Unpaired Training for AFM Image Processing of R2R-Printed CNTs. 1841–1845.

4.

Cha, Eunju, Hyungjin Chung, Jaeduck Jang, et al.. (2022). Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning. ACS Nano. 16(7). 10314–10326. 9 indexed citations

5.

Cha, Eunju, et al.. (2022). Self-Supervised Dense Consistency Regularization for Image-to-Image Translation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18280–18289. 13 indexed citations

6.

Han, Yoseob, Jaeduck Jang, Eunju Cha, et al.. (2021). Deep learning STEM-EDX tomography of nanocrystals. Nature Machine Intelligence. 3(3). 267–274. 39 indexed citations

7.

Cha, Eunju, Eun‐Hee Kang, Jong Chul Ye, et al.. (2020). Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images. Biomedical Signal Processing and Control. 58. 101846–101846. 12 indexed citations

8.

Cha, Eunju, Hyungjin Chung, Eung Yeop Kim, & Jong Chul Ye. (2020). Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution. IEEE Transactions on Medical Imaging. 40(1). 166–179. 10 indexed citations

9.

Cha, Eunju, et al.. (2020). Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction. IEEE Journal of Selected Topics in Signal Processing. 14(6). 1292–1305. 9 indexed citations

10.

Ye, Jong Chul, Yoseob Han, & Eunju Cha. (2018). Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems. SIAM Journal on Imaging Sciences. 11(2). 991–1048. 213 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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