| Diffusion posterior sampling for general noisy inverse problems H Chung, J Kim, MT Mccann, ML Klasky, JC Ye International Conference on Learning Representations (ICLR), 2023 | 369 | 2023 |
| Improving diffusion models for inverse problems using manifold constraints H Chung, B Sim, D Ryu, JC Ye Advances in Neural Information Processing Systems (NeurIPS), 2022 | 252 | 2022 |
| Score-based diffusion models for accelerated MRI H Chung, JC Ye Medical Image Analysis 80 (102479), 2022 | 244 | 2022 |
| Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction H Chung, B Sim, JC Ye Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 241* | 2022 |
| Unpaired deep learning for accelerated MRI using optimal transport driven CycleGAN G Oh, B Sim, HJ Chung, L Sunwoo, JC Ye IEEE Transactions on Computational Imaging 6, 1285-1296, 2020 | 86 | 2020 |
| MR image denoising and super-resolution using regularized reverse diffusion H Chung, ES Lee, JC Ye IEEE Transactions on Medical Imaging 42 (4), 922-934, 2022 | 72 | 2022 |
| Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective M Akçakaya, B Yaman, H Chung, JC Ye IEEE Signal Processing Magazine 39 (2), 28-44, 2022 | 62* | 2022 |
| Solving 3d inverse problems using pre-trained 2d diffusion models H Chung, D Ryu, MT McCann, ML Klasky, JC Ye Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 55 | 2023 |
| Parallel diffusion models of operator and image for blind inverse problems H Chung, J Kim, S Kim, JC Ye Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 49 | 2023 |
| Deep learning STEM-EDX tomography of nanocrystals Y Han, J Jang, E Cha, J Lee, H Chung, M Jeong, TG Kim, BG Chae, ... Nature Machine Intelligence 3 (3), 267-274, 2021 | 34 | 2021 |
| Generative ai for medical imaging: extending the monai framework WHL Pinaya, MS Graham, E Kerfoot, PD Tudosiu, J Dafflon, V Fernandez, ... arXiv preprint arXiv:2307.15208, 2023 | 33 | 2023 |
| Progressive deblurring of diffusion models for coarse-to-fine image synthesis S Lee, H Chung, J Kim, JC Ye NeurIPS 2022 Workshop on Score-Based Methods 16 2022, 2022 | 30 | 2022 |
| Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems H Chung, S Lee, JC Ye arXiv preprint arXiv:2303.05754, 2023 | 29* | 2023 |
| Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison JY Nam, HJ Chung, KS Choi, H Lee, TJ Kim, H Soh, EA Kang, SJ Cho, ... Gastrointestinal Endoscopy 95 (2), 258-268. e10, 2022 | 24 | 2022 |
| Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain H Chung, J Huh, G Kim, YK Park, JC Ye IEEE Transactions on Computational Imaging 7, 747-758, 2021 | 23* | 2021 |
| Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data H Chung, E Cha, L Sunwoo, JC Ye Medical Image Analysis 71, 102047, 2021 | 22 | 2021 |
| Direct diffusion bridge using data consistency for inverse problems H Chung, J Kim, JC Ye Advances in Neural Information Processing Systems 36, 2024 | 20 | 2024 |
| Improving 3D imaging with pre-trained perpendicular 2D diffusion models S Lee, H Chung, M Park, J Park, WS Ryu, JC Ye Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 20 | 2023 |
| Unpaired training of deep learning tMRA for flexible spatio-temporal resolution E Cha, H Chung, EY Kim, JC Ye IEEE Transactions on Medical Imaging 40 (1), 166-179, 2020 | 18 | 2020 |
| Prompt-tuning latent diffusion models for inverse problems H Chung, JC Ye, P Milanfar, M Delbracio arXiv preprint arXiv:2310.01110, 2023 | 16 | 2023 |
Jong Chul YeProfessor and Endowed Chair, KAIST在 kaist.ac.kr 的电子邮件经过验证
