Publications

Hyungjin Chung, Ph.D., Jong Chul Ye (2024). Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems. ECCV 2024.

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Hyungjin Chung, Ph.D., Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio (2024). Prompt-tuning Latent Diffusion Models for Inverse Problems. ICML 2024.

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Hyungjin Chung, Ph.D., Suhyeon Lee, Jong Chul Ye (2024). Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems. ICLR 2024.

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Hyungjin Chung, Ph.D., Jeongsol Kim, Jong Chul Ye (2023). Direct Diffusion Bridge for Inverse Problems with Data Consistency. In NeurIPS 2023.

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Suhyeon Lee, Hyungjin Chung, Ph.D., Minyoung Park, Jonghyuk Park, Wi-Sun Ryu, Jong Chul Ye (2023). Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models. In ICCV 2023.

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Hyungjin Chung, Ph.D., Dohoon Ryu, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye (2023). Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models. In CVPR 2023.

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Hyungjin Chung, Ph.D., Jeongsol Kim, Sehui Kim, Jong Chul Ye (2023). Parallel Diffusion Models of Operator and Image for Blind Inverse Problems. In CVPR 2023.

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Hyungjin Chung, Ph.D., Jeongsol Kim, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye (2023). Diffusion Posterior Sampling for General Noisy Inverse Problems. ICLR 2023 (SPOTLIGHT).

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Hyungjin Chung, Ph.D., Byeongsu Sim, Dohoon Ryu, Jong Chul Ye (2022). Improving Diffusion Models for Inverse Problems using Manifold Constraints. In NeurIPS 2022.

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Eunju Cha, Hyungjin Chung, Ph.D., Jaeduck Jang, Junho Lee, Eunha Lee, Jong Chul Ye (2022). Low-dose sparse-view HAADF-STEM-EDX tomography of nanocrystals using unsupervised deep learning. In ACS Nano.

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Hyungjin Chung, Ph.D., Byeongsu Sim, Jong Chul Ye (2022). Come-Closer-Diffuse-Faster Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction. In CVPR 2022.

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Joon Yeul Nam, Hyungjin Chung, Ph.D., Kyu Sung Choi, Hyuk Lee, Seung Jun Han, Tae Jun Kim, Hosim Soh, Eun Kang, Soo_jeong Cho, Jong Chul Ye, Jong Pil Im, Sang Gyun Kim, Yoon Jun Kim, Joo Sung Kim, Jung-Hwan Yoo, Hyunsoo Chung, Jeong-Hoon Lee (2021). Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison. GIE.

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Hyungjin Chung, Ph.D., Jong Chul Ye (2021). Score-based diffusion models for accelerated MRI. In MedIA.

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Mehmet Akcakaya, Burhaneddin Yaman, Hyungjin Chung, Ph.D., Jong Chul Ye (2021). Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement. IEEE SPM.

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Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Ph.D., Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye (2021). Deep learning STEM-EDX tomography of nanocrystals. In Nat. Mach. Int..

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Eunju Cha, Hyungjin Chung, Ph.D., Eung Yeop Kim, Jong Chul Ye (2020). Unpaired training of deep learning tMRA for flexible spatio-temporal resolution. IEEE TMI.

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Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Ph.D., Leonard Sunwoo, Jong Chul Ye (2020). Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN. IEEE TCI.

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Hyungjin Chung, Ph.D., Jaehyun Kim, Jeong Hee Yoon, Jeong Min Lee, Jong Chul Ye (2020). Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning. In ArXiv.

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Hyungjin Chung, Ph.D., Jong Chul Ye (2020). Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN. In Nat. Mach. Int..

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Hyungjin Chung, Ph.D., Eunju Cha, Leonard Sunwoo, Jong Chul Ye (2020). Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data. In Medical Image Analysis.

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Joonhyung Lee, Hyunjong Kim, Hyungjin Chung, Jong Chul Ye (2020). Deep Learning Fast MRI Using Channel Attention in Magnitude Domain. IEEE ISBI.

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Hyungjin Chung, Ph.D., Jaeyoung Huh, Geon Kim, Yong Keun Park, Jong Chul Ye (2020). Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain. In IEEE TCI.

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