Main Publications
Other Publications

Book

Jong Chul Ye, Geometry of Deep Learning: A Signal Processing Perspective,  Springer,  ISBN-13: 978-9811660450
Jong Chul Ye,  Yonina C. Eldar, Michael Unser,   Deep Learning for Biomedical Image Reconstruction, Cambridge University Press

Top-tier ML Conf. (NeurIPS, ICML, ICLR, CVPR, ICCV,ECCV, MICCAI)

  1. Bryan Sangwoo Kim, Jeongsol Kim, Jong Chul Ye, "Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment", Conference on Neural Information Processing Systems (NeurIPS), 2025. (Spotlight)
  2. Jaa-Yeon Lee, ByungHee Cha, Jeongsol Kim, Jong Chul Ye,"Aligning Text to Image in Diffusion Models is Easier Than You Think", Conference on Neural Information Processing Systems (NeurIPS), 2025.
  3. Jiachen Yao, Abbas Mammadov, Julius Berner, Gavin Kerrigan, Jong Chul Ye, Kamyar Azizzadenesheli, Anima Anandkumar, "Guided Diffusion Sampling on Function Spaces with Applications to PDEs", Conference on Neural Information Processing Systems (NeurIPS), 2025.
  4. Noam Elata, Hyungjin Chung, Jong Chul Ye, Tomer Michaeli, Michael Elad,"InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems", ​Conference on Neural Information Processing Systems (NeurIPS), 2025.
  5. Hyeonho Jeong, Suhyeon Lee, Jong Chul Ye, "Reangle-A-Video: 4D Video Generation as Video-to-Video Translation",   Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2025, Hawaii.
  6. Taesung Kwon, Jong Chul Ye, "VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models",  Proceedings of the IEEE International Conference  on Computer Vision (ICCV) 2025, Hawaii.
  7. Jaemin Kim, Bryan Sangwoo Kim, Jong Chul Ye, "Free2Guide: Training-Free Text-to-Video Alignment using Image LVLM",  Proceedings of the IEEEE International Conference on Computer Vision (ICCV) 2025, Hawaii.
  8. Jeongsol Kim, Bryan Sangwoo Kim, Jong Chul Ye,"FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems", Proceedings of the IEEEE International Conference on Computer Vision (ICCV) 2025, Hawaii.
  9. Geon Yeong Park, Sang Wan Lee, Jong Chul Ye, "Inference-Time Diffusion Model Distillation",Proceedings of the IEEEE International Conference on Computer Vision (ICCV) 2025, Hawaii.
  10. Soobin Um, Beomsu Kim, and Jong Chul Ye, "Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation", International Conference on Machine Learning (ICML), 2025.
  11. Jinho Chang, and Jong Chul Ye,"LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Space Surpass the AR Models", International Conference on Machine Learning (ICML), 2025.
  12. Soobin Um, Jong Chul Ye, "Minority-Focused Text-to-Image Generation via Prompt Optimization", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025 (selected as oral presentation, top 0.74%).
  13. Hyelin Nam, Jaemin Kim, Dohun Lee, Jong Chul Ye,"Optical-Flow Guided Prompt Optimization for Coherent Video Generation", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  14. Won Jun Kim, Hyungjin Chung, Jaemin Kim, Sangmin Lee, Byeongsu Sim, Jong Chul Ye,"Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  15. Dohun Lee, Bryan Sangwoo Kim, Geon Yeong Park, Jong Chul Ye,"VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  16. Hyeonho Jeong, Chun-Hao Paul Huang, Jong Chul Ye, Niloy Mitra, Duygu Ceylan, "Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  17. Taesung Kwon, Jong Chul Ye, "Solving Video Inverse Problems Using Image Diffusion Models", The Thirteenth International Conference on Learning Representations (ICLR), 2025
  18. Hyungjin Chung, Jeongsol Kim, Geon Yeong Park, Hyelin Nam, Jong Chul Ye, "CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models", The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  19. Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, Jong Chul Ye, "Regularization by Texts for Latent Diffusion Inverse Solvers", The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  20. Serin Yang, Taesung Kwon, Jong Chul Ye, "ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler", The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  21. Gihyun Kwon, Jong Chul Ye,"TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation", The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  22. Beomsu Kim, Jaemin Kim, Jeongsol Kim, Jong Chul Ye,"Generalized Consistency Trajectory Models for Image Manipulation", The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  23. Beomsu Kim, Yu-Guan Hsieh, Michal Klein, marco cuturi, Jong Chul Ye, Bahjat Kawar, James Thornton,"Simple ReFlow: Improved Techniques for Fast Flow Models",The Thirteenth International Conference on Learning Representations (ICLR), 2025.
  24. Geon Yeong Park, Hyeonho Jeong. Sang Wan Lee, Jong Chul Ye, "Spectral Motion Alignment for Video Motion Transfer using Diffusion Models",  in Proceedings of The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2025.
  25. Hyeonho Jeong, Jinho Chang, Geon Yeong Park, Jong Chul Ye. "DreamMotion: Space-Time Self-Similarity Score Distillation for Zero-Shot Video Editing",  in Proceedings of 2024 European Conference on Computer Vision (ECCV).
  26. Soobin Um, and Jong Chul Ye "Self-Guided Generation of Minority Samples Using Diffusion Models",  in Proceedings of 2024 European Conference on Computer Vision (ECCV).
  27. Hyungjin Chung and Jong Chul Ye, "Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems",  in Proceedings of 2024 European Conference on Computer Vision (ECCV).
  28. Kwanyoung Kim, Yujin Oh, and Jong Chul Ye, "OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation",  in Proceedings of 2024 European Conference on Computer Vision (ECCV).
  29. Jeongsol Kim, Geon Yeong Park, and Jong Chul Ye, "DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation", in Proceedings of 2024 European Conference on Computer Vision (ECCV).
  30. Sangmin Lee, Abbas Mammadov, Jong Chul Ye, "Defining Neural Network Architecture through Polytope Structures of Dataset",  in the Proceedings of The  International Conference on Machine Learning (ICML), 2024.
  31. Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio, "Prompt-tuning latent diffusion models for inverse problems",   in the Proceedings of The  International Conference on Machine Learning (ICML), 2024.
  32. Hyelin Nam, Gihyun Kwon, Geon Yeong Park, Jong Chul Ye,"Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing", in the Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
  33. Hyeonho Jeong, Geon Yeong Park, Jong Chul Ye, "One-Shot Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models", in the Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
  34. Geon Yeong Park, Chanyong Jung, Sangmin Lee, Jong Chul Ye, Sang Wan Lee,"Self-supervised debiasing using low rank regularization", in the Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
  35. Gihyun Kwon, Simon Jenni, Dingzeyu Li, Joon-Young Lee, Jong Chul Ye, Fabian Caba Heilbron, "Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models",  in the Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
  36. Hyeonho Jeong, Jong Chul Ye,"Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models", International Conference on Learning Representations (ICLR), 2024.
  37. Hyungjin Chung, Suhyeon Lee, Jong Chul Ye,,  "Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems", International Conference on Learning Representations (ICLR), 2024.
  38. Beomsu Kim, Gihyun Kwon, Kwanyoung Kim, Jong Chul Ye,"Unpaired Image-to-Image Translation via Neural Schrödinger Bridge",  International Conference on Learning Representations (ICLR), 2024.
  39. Soobin Um, Suhyeon Lee, Jong Chul Ye,"Don't Play Favorites: Minority Guidance for Diffusion Models", International Conference on Learning Representations (ICLR), 2024.
  40. JangHo Park, Gihyun Kwon, Jong Chul Ye,"ED-NeRF: Efficient Text-Guided Editing of 3D Scene With Latent Space NeRF", International Conference on Learning Representations (ICLR), 2024.
  41. Suhyeon Lee, Won Jun Kim, Jinho Chang, Jong Chul Ye,"LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation",International Conference on Learning Representations (ICLR), 2024.
  42. ​Chanyong Jung, Gihyun Kwon, Jong Chul Ye, "Patch-Wise Graph Contrastive Learning for Image Translation", The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024
  43. Geon Yeong Park, Jeongsol Kim, Beomsu Kim, Sang Wan Lee, Jong Chul Ye ,"Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models",  Conference on Neural Information Processing Systems (NeurIPS), 2023 
  44. Hyungjin Chung, Jeongsol Kim, Jong Chul Ye, "Direct Diffusion Bridge using Data Consistency for Inverse Problems", Conference on Neural Information Processing Systems (NeurIPS), 2023 
  45. Yang, Serin, Hyunmin Hwang, and Jong Chul Ye. "Zero-shot contrastive loss for text-guided diffusion image style transfer," IEEE/CVF International Conference on Computer Vision (ICCV) 2023.
  46. Lee, S., Chung, H., Park, M., Park, J., Ryu, W. S., & Ye, J. C.  "Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models",  IEEE/CVF International Conference on Computer Vision (ICCV) 2023.
  47. Beomsu Kim, and Jong Chul Ye, "Denoising MCMC for Accelerating Diffusion-Based Generative Models", International Conference on Machine Learning (ICML),  oral presentation, 2023 
  48. Lee, Sangyun, Beomsu Kim, and Jong Chul Ye. "Minimizing Trajectory Curvature of ODE-based Generative Models." International Conference on Machine Learning (ICML), (2023).
  49. Geon Yeong Park, Sangmin Lee, Sang Wan Lee, Jong Chul Ye, "Training debiased subnetworks with contrastive weight pruning", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  50. Hyungjin Chung, Jeongsol Kim, Sehui Kim, Jong Chul Ye,"Parallel Diffusion Models of Operator and Image for Blind Inverse Problems", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  51. Hyungjin Chung, Dohoon Ryu, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye,"Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  52. Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, Jong Chul Ye, "Diffusion Posterior Sampling for General Noisy Inverse Problems", International Conference on Learning Representations (ICLR), 2023  Spotlight (notable-top-25%) Presentation.
  53. Boah Kim, Yujin Oh, Jong Chul Ye,"Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation", International Conference on Learning Representations (ICLR), 2023 
  54. Gihyun Kwon, Jong Chul Ye,"Diffusion-based Image Translation using disentangled style and content representation",  International Conference on Learning Representations (ICLR), 2023 ​
  55. Lee, Sangyun, Hyungjin Chung, Jaehyeon Kim, and Jong Chul Ye. "Progressive deblurring of diffusion models for coarse-to-fine image synthesis,"  NeurIPS 2022 Workshop on Score-Based Methods, 2022.
  56. Beomsu Kim, Jong Chul Ye, “Energy-Based Contrastive Learning of Visual Representations”, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022.
  57. Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye, “Improving Diffusion Models for Inverse Problems using Manifold Constraints”,Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022.
  58. Yujin Oh, and Jong Chul Ye, "CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation", European Conference on Computer Vision (ECCV), 2022.
  59. Boah Kim, Inhwa Han, and Jong Chul Ye, "DiffuseMorph: Unsupervised Deformable Image Registration  Using Diffusion Models",European Conference on Computer Vision (ECCV), 2022.
  60. Chanyong Jung, Joonhyung Lee, Sun Kyoung You, Jong Chul Ye, "Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising", International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  61. Boah Kim and Jong Chul Ye, “Diffusion deformable model for 4D temporal medical image generation”, the 25nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
  62. Gwanghyun Kim, and Jong Chul Ye. "DiffusionCLIP: Text-guided image manipulation using diffusion models," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  63. Chanyong Jung, Gihyun Kwon, Jong Chul Ye, "Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks",IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  64. Gihyun Kwon, and Jong Chul Ye. "CLIPstyler: Image style transfer with a single text condition,"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  65. Kwanyoung Kim, Taesung Kwon, and Jong Chul Ye. "Noise distribution adaptive self-supervised image denoising using Tweedie distribution and score matching", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  66. Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye. "Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction",IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  67. Kwanyoung Kim, Jong Chul Ye, "Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images",   in Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 2021.
  68. Sangjoon Park*, Gwanghyun Kim*, Jeongsol Kim, Boah Kim, Jong Chul Ye, "Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis",  in Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 2021. (*co-first authors)
  69. Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim, "Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention",   in Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 2021.
  70. Kwon Gihyun, Jong Chul Ye, "Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation",  IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
  71. Ignatov, Andrey, et al. "AIM 2020 challenge on learned image signal processing pipeline." European Conference on Computer Vision. Springer, Cham, 2020.
  72. ​Kim, Byung-Hoon, et al. "Pynet-ca: enhanced pynet with channel attention for end-to-end mobile image signal processing." European Conference on Computer Vision. Springer, Cham, 2020.
  73. Zhang, Kai, et al "NTIRE 2020 challenge on perceptual extreme super-resolution: Methods and results." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2020.​​
  74. Kim, Boah, et al. "Unsupervised deformable image registration using cycle-consistent CNN." International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019.
  75. Khan, Shujaat, Jaeyoung Huh, and Jong Chul Ye. "Deep learning-based universal beamformer for ultrasound imaging." International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019.
  76. Ye, Jong Chul, and Woon Kyoung Sung. "Understanding Geometry of Encoder-Decoder CNNs."  International Conference on Machine Learning (ICML), 2019.
  77. Lee, Dongwook, Junyoung Kim, Won-Jin Moon, and Jong Chul Ye. "CollaGAN: Collaborative GAN for Missing Image Data Imputation." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral Presentation, Best Paper Finalist)
  78. Timofte, Radu, et al. "NTIRE 2017 challenge on single image super-resolution: Methods and results." Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW). 2017.
  79. Bae, Woong, Jaejun Yoo, and Jong Chul Ye. "Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification." Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW). 2017.
  80. Lim, Jae Hyun, and Jong Chul Ye. "Geometric GAN."  International Conference on Machine Learning (ICML) Workshop (2017).

Peer-Reviewed Journal Papers

  1. Dongjin Seo, Soobin Um, Sangbin Lee, Jong Chul Ye, Haejun Chung, "Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models", ACS Photonics, 2025 (in press).
  2. Taesung Kwon, Gookho Song, Yoosun Kim, Jong Chul Ye, Mooseok Jang,"Video Diffusion Posterior Sampling for Seeing Beyond Dynamic Scattering Layers",  IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2025 (in press).
  3. Kwanyoung Kim, Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Joongyo Lee, Jin Sung Kim, Yong Bae Kim, Jong Chul Ye,"End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding", Medical Image Analysis (in press), 2025.
  4. Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae Hoon Roh, Simon Arridge, Peter Maass, Bangti Jin, Jong Chul Ye, "Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction', IEEE Trans. Medical Imaging (in press), 2025.
  5. Choe, Jooae et al. "Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion",  European Journal of Radiology (in press), 2025.
  6. Sehui Kim, Hyungjin Chung, Se Hie Park, Eui-Sang Chung, Kayoung Yi, and Jong Chul Ye, "Fundus image enhancement through direct diffusion bridges", IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3446866, 2024.
  7. Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Yeona Cho, Ik Jae Lee, Jin Sung Kim, and Jong Chul Ye, "LLM-driven Multimodal Target Volume Contouring in Radiation Oncology", Nature Communications 15 (1), 9186,2024.
  8. S. Park, I. J. Lee, J. W. Kim and J. Chul Ye, "MS-DINO: Masked Self-Supervised Distributed Learning Using Vision Transformer," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 10, pp. 6180-6192, Oct. 2024
  9. Sangmin Lee and Jong Chul Ye, "Magnitude and Angle Dynamics in Training Single ReLU Neurons", Neural Networks (in press), 2024.
  10. Oh, J., Kim, B., Oh, G., Hwangbo, Y., & Ye, J. C. "End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography", Endocrinology and Metabolism, 2024.
  11. Kinoshita D, Suzuki K, Yuki H, Niida T, Fujimoto D, Minami Y, Dey D, Lee H, McNulty I, Ako J, Ferencik M, Kakuta T, Ye JC, Jang IK. Coronary plaque phenotype associated with positive remodeling. J Cardiovasc Comput Tomogr. 2024 Jul-Aug;18(4):401-407.
  12. Chang, J., Ye, J.C. Bidirectional generation of structure and properties through a single molecular foundation model. Nat Commun 15, 2323 (2024).
  13. Gyutaek Oh, Yeonsil Moon, Won-Jin Moon,Jong Chul Ye, "Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI without AIF Measurements", NeuroImage  291, 1 May 2024​
  14. G. Oh, S. Jung, J. E. Lee and J. C. Ye, "Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction," in IEEE Transactions on Computational Imaging, vol. 10, pp. 43-53, 2024.
  15. J. Huh, S. Park, J. E. Lee and J. C. Ye, "Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1692-1703, March 2024.
  16. Boah Kim, Yujin Oh, Bradford J. Wood, Ronald M. Summers, Jong Chul Ye, "C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation", Medical Image Analysis, Vol. 91: 103022, January 2024
  17. Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye, "Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology", Medical Image Analysis ,Vol. 91,  103021, January 2024
  18. ​Sangjoon Park*, Haruhito Yuki*, Takayuki Niida, Keishi Suzuki, Daisuke Kinoshita, Iris McNulty, Alexander Broersen, Jouke Dijkstra, Hang Lee, Tsunekazu Kakuta, *Jong Chul Ye, *Ik-Kyung Jang, “A Novel Deep Learning Model for a Computed Tomography Diagnosis of Coronary Plaque Erosion”, Scientific Reports  13, 22992 (2023)
  19. Gihyun Kwon, and Jong Chul Ye, "One-Shot Adaptation of GAN in Just One CLIP", IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI)   vol. 45, pp. 12179-12191, Oct. 2023.
  20. Yujin Oh, Go Eun Bae, Kyung-Hee Kim, Min-Kyung Yeo, Jong Chul Ye, "Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 8, pp. 4143-4153, Aug. 2023.
  21. W. C. Karl, J. E. Fowler, C. A. Bouman, M. Çetin, B. Wohlberg and J. C. Ye, "The Foundations of Computational Imaging: A signal processing perspective," in IEEE Signal Processing Magazine, vol. 40, no. 5, pp. 40-53, July 2023.
  22. HWANG, Hye Jeon, et al. Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease. Korean Journal of Radiology (IF=7.109), 2023, 24.8: 807-820.
  23. Park S, Ye JC, Lee ES, Cho G, Yoon JW, Choi JH, Joo I, Lee YJ. Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning. Korean J Radiol. 24(6):541-552, June 2023.
  24. Araki, M., Park, S., Nakajima, A., Lee, H., Ye, J. C., & Jang, I. K. Diagnosis of coronary layered plaque by deep learning. Scientific Reports, 13(1), 2432, 2023.
  25. B. Kim, J. Kim and J. C. Ye, "Task-Agnostic Vision Transformer for Distributed Learning of Image Processing," in IEEE Transactions on Image Processing, vol. 32, pp. 203-218, 2023​
  26. H. Chung, E. S. Lee and J. C. Ye, "MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion," in IEEE Transactions on Medical Imaging, vol. 42, no. 4, pp. 922-934, April 2023.
  27. Lee, C., Song, G., Kim, H. et al. Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat Mach Intell 5, 35–45 (2023).
  28. Joonyoung Song, and Jong Chul Ye, "Wavelet Subband Discriminator for Efficient UnsupervisedChest X-ray Image Restoration",  Medical Physics,50(4):2263-2278,  April 2023.
  29. S. Park and J. C. Ye, "Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation," in IEEE Transactions on Medical Imaging, vol. 42, no. 7, pp. 2091-2105, July 2023.
  30. Jaeyoung Huh, Shujaat Khan, Sungjin Choi, Dongkuk Shin, Jeong Eun Lee, Eun Sun Lee∗, Jong Chul Ye*, "Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN", Medical Image Analysis. 83:102651, Jan, 2023.
  31. Z. Zhao, J. C. Ye and Y. Bresler, "Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications," in IEEE Signal Processing Magazine, vol. 40, no. 1, pp. 148-163, Jan. 2023.
  32. A. Wahab, S. Khan, I. Naseem and J. C. Ye, "Performance Analysis of Fractional Learning Algorithms," in IEEE Transactions on Signal Processing, vol. 70, pp. 5164-5177, 2022.
  33. Kim H, Oh G, Seo JB, Hwang HJ, Lee SM, Yun J, Ye JC. Multi-domain CT translation by a routable translation network. Physics in Medicine & Biology. 2022 Sep 26.
  34. Sangjoon Park, Makoto Araki, Akihiro Nakajima, Hang Lee, Valentin Fuster, Jong Chul Ye, Ik-Kyung Jang, "Enhanced diagnosis of plaque erosion by deep learning in patients with acute coronary syndromes", JACC: Cardiovascular Interventions, Volume 15, Issue 20, Pages 2020-2031, 24 October 2022.
  35. Cha, Eunju; Chung, Hyungjin; Jang, Jaeduck; Lee, Junho; Lee, Eunha; Ye, Jong Chul, "Low-dose sparse-view HAADF-STEM-EDX tomography of nanocrystals using unsupervised deep learning", ACS Nano 2022, 16, 7, 10314–1032, June 2022.
  36. Sangjoon Park, Gwanghyun Kim, Yujin Oh, Joon Beom Seo, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Chang Min Park, Jong Chul Ye, Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation. Nat Commun 13, 3848 (2022)
  37. Park, H., Na, M., Kim, B. et al. Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy. Nat Commun 13, 3297 (2022).
  38. Hyungjin Chung, Jong Chul Ye, "Score-based diffusion models for accelerated MRI", Medical Image Analysis Volume 80, 102479, August 2022.
  39. Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, Won Jin Moon, Jong Chul Ye,"Unsupervised Resolution-Agnostic Quantitative Susceptibility Mapping using Adaptive Instance Normalization," Medical Image Analysis Volume 79, 102477, July 2022.
  40. ​Moinuddin, M., Khan, S., Alsaggaf, A. U., Abdulaal, M. J., Al-Saggaf, U. M., & Ye, J. C. (2022). Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network. Frontiers in Physiology, 2326.
  41. Araki, M., Park, SJ., Dauerman, H.L. et al. Optical coherence tomography in coronary atherosclerosis assessment and intervention. Nat Rev Cardiol 19, 684–703 (2022).
  42. M. Akçakaya, B. Yaman, H. Chung and J. C. Ye, "Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective," in IEEE Signal Processing Magazine, vol. 39, no. 2, pp. 28-44, March 2022, doi: 10.1109/MSP.2021.3119273.
  43. S. Khan, J. Huh and J. C. Ye, "Switchable and Tunable Deep Beamformer Using Adaptive Instance Normalization for Medical Ultrasound," in IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 266-278, Feb. 2022, doi: 10.1109/TMI.2021.3110730.
  44. Nam JY, Chung HJ, Choi KS, Lee H, Kim TJ, Soh H, Kang EA, Cho SJ, Ye JC, Im JP, Kim SG, Kim JS, Chung H, Lee JH. Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison. Gastrointest Endosc. 2022 Feb;95(2):258-268.e10. doi: 10.1016/j.gie.2021.08.022. Epub 2021 Sep 4. PMID: 34492271.
  45. Sangjoon Park, Gwanghyun Kim, Yujin Oh, Joon Beom Se, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Jong Chul Ye,"Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification", Medical Image Analysis, Vol. 75, January 2022, 102299
  46. Eunju Cha, Chanseok Lee, Mooseok Jang, and Jong Chul Ye, "DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrieval", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9931-9943, 1 Dec. 2022.
  47. Dhaval Kolte , Taishi Yonetsu , Jong Chul Ye , Peter Libby , Valentin Fuster, Ik-Kyoung Jang, "Plaque Erosion: Pathobiology, Diagnosis, and Clinical Implications",  Journal of the American College of Cardiology (in press),  2021
  48. T. Kwon and J. C. Ye, "Cycle-Free CycleGAN Using Invertible Generator for Unsupervised Low-Dose CT Denoising," in IEEE Transactions on Computational Imaging, vol. 7, pp. 1354-1368, 2021, doi: 10.1109/TCI.2021.3129369.
  49. Chung, H., Ye, J.C. "Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN". Nature Mach Intell 3, 861–863 (2021). https://doi.org/10.1038/s42256-021-00400-4
  50. Jawook Gu, Tae Seong Yang, Jong Chul Ye, Dong Hyun Yang, "CycleGAN denoising of extreme low-dose cardiac CT using wavelet-assisted noise disentanglement",  Medical Image Analysis, 74, 102209, 2021.
  51. J. Lee, J. Gu and J. C. Ye, "Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN," in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3932-3944, Dec. 2021, doi: 10.1109/TMI.2021.3101363. 
  52. H. Chung, J. Huh, G. Kim, Y. K. Park and J. C. Ye, "Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain," in IEEE Transactions on Computational Imaging, vol. 7, pp. 747-758, 2021, doi: 10.1109/TCI.2021.3098937.
  53. G. Oh, J. E. Lee and J. C. Ye, "Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 3125-3139, Nov. 2021, doi: 10.1109/TMI.2021.3089708
  54. S. Yang, E. Y. Kim and J. C. Ye, "Continuous Conversion of CT Kernel Using Switchable CycleGAN With AdaIN," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 3015-3029, Nov. 2021, doi: 10.1109/TMI.2021.3077615
  55. Kim, Y., Oh, D.Y., Chang, W. et al. "Deep learning–based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study". Eur Radiol 31, 8755–8764 (2021). https://doi.org/10.1007/s00330-021-07810-3
  56. Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye, "Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data,"  Medical Image Analysis, 71, 102047, 2021.
  57. Joonyoung Song, Jae-Heon Jeong, Dae-Soon Park, Hyun-Ho Kim, Doo-Chun Seo, Jong Chul Ye, "Unsupervised Denoising for Satellite Imagery using Wavelet Directional CycleGAN",  IEEE Trans. on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6823-6839, Aug. 2021,
  58. ​Kim, Boah, Dong Hwan Kim, Seong Ho Park, Jieun Kim, June-Goo Lee, and Jong Chul Ye. "CycleMorph: Cycle consistent unsupervised deformable image registration." Medical Image Analysis, vol 71, July 2021, 102036
  59. S. Khan, J. Huh and J. C. Ye, "Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 6, pp. 2086-2100, June 2021
  60. Ryu, D., Ryu, D., Baek, Y., Cho, H., Kim, G., Kim, Y.S., Lee, Y., Kim, Y., Ye, J.C., Min, H.S. and Park, Y.,  "DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning",IEEE Transactions on Medical Imaging, vol. 40, no. 5, pp. 1508-1518, May 2021.
  61. J. Gu and J. C. Ye, "AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising," in IEEE Transactions on Computational Imaging, vol. 7, pp. 73-85, 2021, doi: 10.1109/TCI.2021.3050266.
  62. Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee,  Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee and Jong Chul Ye,"Deep learning STEM-EDX tomography of nanocrystals", Nature Machine Intelligence, 1-8, Feb., 2021
  63. Eunju Cha, Hyungjin Chung, Eung Yeop Kim, and Jong Chul Ye, "Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution",   IEEE Trans. on Medical Imaging, Vol. 40, no. 1, pp. 166-179, Jan. 2021
  64. Wang, Ge, Jong Chul Ye, and Bruno De Man. "Deep learning for tomographic image reconstruction." Nature Machine Intelligence 2, 737-748, Dec., 2020
  65. Sim, B., Oh, G., Kim, J., Jung, C., & Ye, J. C.  Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems. SIAM Journal on Imaging Sciences, 13(4), 2281-2306, Dec., 2020
  66. Y. Han, J. Kim and J. C. Ye, "Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal," in IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3571-3582, Nov. 2020, doi: 10.1109/TMI.2020.3000341.
  67. E. Cha, G. Oh and J. C. Ye, "Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction," in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 6, pp. 1292-1305, Oct. 2020, doi: 10.1109/JSTSP.2020.2982777.
  68. Oh, Gyutaek, Byeongsu Sim, HyungJIn Chung, Leonard Sunwoo, and Jong Chul Ye. "Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN."  IEEE Transactions on Computational Imaging, vol. 6, pp. 1285-1296,  August , 2020
  69. Eun Young Chae, Hak Hee Kim*, Sohail Sabir, Yejin Kim, Hyeongseok Kim, Sungho Yoon, Jong Chul Ye, Seungryong Cho, Duchang Heo, Kee Hyun Kim, Young Min Bae, Young-Wook Choi, " Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection", Scientific Reports, 13127, Aug., 2020.
  70. Kyu Sung Choi, Sung-Hye You, Yoseob Han, Jong Chul Ye, Bumseok Jeong,  Seung Hong Choi, "Improving the reliability of pharmacokinetic parameters in dynamic contrast-enhanced MRI in astrocytomas: Deep learning approach", Radiology, https://doi.org/10.1148/radiol.2020192763, Aug., 2020.
  71. Yujin Oh, Sangjoon Park and Jong Chul Ye, "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2688-2700, Aug. 2020, doi: 10.1109/TMI.2020.2993291.
  72. Sungjun Lim, Hyoungjun Park, Sang-Eun Lee, Sunghoe Chang, Byeongsu Sim, and Jong Chul Ye,"CycleGAN With a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry", IEEE Trans. on Computational Imaging, 6, 1127-1138, July, 2020.
  73. Byung-Hoon Kim, Jong Chul Ye,"Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis", Frontieres in Neuroscience, DOI:10.3389/fnins.2020.00630, June, 2020.
  74. Mi-Sun Kang, Eunju Cha, Eunhee Kang, Jong Chul Ye, Nam-Gu Her, Jeong-Woo Oh, Do-Hyun Nam, Myoung-Hee Kim, and Sejung Yang, Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images. Biomedical Signal Processing and Control, 58, p.101846, April, 2020
  75. Shujaat Khan, Jaeyoung Huh, and Jong Chul Ye,"Adaptive and Compressive Beamforming using Deep Learning for Medical Ultrasound",  IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol 67, No. 8, pp.1558 - 1572, March, 2020.
  76. ​Yoon Joo Shin, Won Chang, Jong Chul Ye,  Eunhee Kang,  Dong Yul Oh,  Yoon Jin Lee,  Ji Hoon Park,  and Young Hoon Kim, "Low-Dose Abdominal CT Computed Tomography Using a Deep Learning-Based Denoising Algorithm: A Compared Comparison with CT Computed Tomography Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm", Korean Journal of Radiology (impact factor 3.73), 21(e15), March, 2020.
  77. ​Won‐Joon Do, Sunghun Seo, Yoseob Han, Jong Chul Ye, Seung Hong Choi, and Sung‐Hong Park, "Reconstruction of Multi‐contrast MR Images through Deep Learning," Medical Physics ,47 (3), 983-997, March, 2020.
  78. Hyun-Seo Ahn, Sung-Hong Park, and Jong Chul Ye, "Quantitative Susceptibility Map Reconstruction Using Annihilating Filter-based Low-Rank Hankel Matrix Approach", Magnetic Resonance in Medicine, 83(3), 858-871, March, 2020
  79. Yoseob Han, Leonard Sunwoo, and Jong Chul Ye, "k-Space Deep Learning for Accelerated MRI", IEEE Trans. on Medical Imaging, 39(2), 377-386, Feb. 2020
  80. Donwook Lee, Won-Jin Moon, and Jong Chul Ye, "Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks", Nature Machine Intelligence, 2, 34-42, January, 2020
  81. Mathews Jacob, Merry P. Mani, and Jong Chul Ye,"Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning", IEEE Signal Processing Magazine, 37(1), 54-68, January, 2020.
  82. B. Kim and J. C. Ye, "Mumford–Shah Loss Functional for Image Segmentation With Deep Learning," in IEEE Transactions on Image Processing, vol. 29, pp. 1856-1866, 2020, doi: 10.1109/TIP.2019.2941265.
  83. S. Ravishankar, J. C. Ye and J. A. Fessler, "Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning," in Proceedings of the IEEE, vol. 108, no. 1, pp. 86-109, Jan. 2020, doi: 10.1109/JPROC.2019.2936204.
  84. Juyoung Lee, Yoseob Han, Jae-Kyun Ryu, Jang-Yeon Park and Jong Chul Ye, "k-Space Deep Learning for Reference-free EPI Ghost Correction", Magnetic Resonance in Medicine, 82(6), 2299-2313, December, 2019
  85. Yoseob Han and Jong Chul Ye, "One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection", Medical Physics, 46(12), 855-872, December, 2019
  86. Jong Chul Ye, "Compressed Sensing MRI: A Review from Signal Processing Perspective", BMC Biomedical Engineering (invited review for the inaugural issue), 1(1), p.8, December,  2019.
  87. Jaejun Yoo, Sohail Sabir, Duchang Heo, Kee Hyun Kim, Abdul Wahab , Yoonseok Choi, Seul-I Lee, Eun Young Chae, Hak Hee Kim, Young Min Bae, Young-Wook Choi, Seungryong Cho, and Jong Chul Ye, "Deep Learning for Diffuse Optical Tomography", IEEE Trans., on Medical Imaging,  39 (4), 877-887, August, 2019
  88. Eunhee Kang, Hyun Jung Koo, Dong Hyun Yang, Joon Bum Seo and Jong Chul Ye, "Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography",   Medical physics 46, no. 2, pp. 550-562. Feb., 2019
  89. Y. H. Yoon, S. Khan, J. Huh and J. C. Ye, "Efficient B-Mode Ultrasound Image Reconstruction From Sub-Sampled RF Data Using Deep Learning," in IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 325-336, Feb. 2019, doi: 10.1109/TMI.2018.2864821.
  90. Jaejun Yoo, Abdul Wahab, and Jong Chul Ye, "A Mathematical Framework for Deep Learning in Elastic Source Imaging", SIAM Journal on Applied Mathematics 78(5), 2791–2818, 2018
  91. Junhong Min,  Kyoung Hwan Jin, Michael Unser, and Jong Chul Ye, "Grid-Free Localization Algorithm Using Low Rank Hankel Matrix For Super-Resolution Microscopy", IEEE Trans. on Image Processing, Volume: 27, Issue: 10, 4771 - 4786, Oct. 2018.
  92. K. Lee, Y. Li, K. H. Jin and J. C. Ye, "Unified Theory for Recovery of Sparse Signals in a General Transform Domain," in IEEE Transactions on Information Theory, vol. 64, no. 8, pp. 5457-5477, Aug. 2018, doi: 10.1109/TIT.2018.2846643.
  93. Ge Wang, Jong Chul Ye, Klaus Mueller, Jeffrey A Fessler, "Image Reconstruction Is a New Frontier of Machine Learning",  IEEE Trans. on Medical Imaging,  Vol. 37 no. 6, pp. 1289 - 1296, June 2018.
  94. Y. Han and J. C. Ye, "Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT," in IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1418-1429, June 2018, doi: 10.1109/TMI.2018.2823768.  (MatConvNet implementation)
  95. E. Kang, W. Chang, J. Yoo and J. C. Ye, "Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network," in IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1358-1369, June 2018, doi: 10.1109/TMI.2018.2823756.. (MatConvNet implementation)
  96. D. Lee, J. Yoo, S. Tak and J. C. Ye, "Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks," in IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 1985-1995, Sept. 2018, doi: 10.1109/TBME.2018.2821699.
  97. Jong Chul Ye, Yoseob Han and Eunju Cha, "Deep convolutional framelets: a general deep learning framework for inverse problems",  SIAM Journal on Imaging Sciences 11(2), 991–1048, 2018.
  98. Yoseob Han, Jaejun Yoo, Hak Hee Kim, Hee Jung Shin, Kyunghyun Sung, and Jong Chul Ye, "Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR",  Magnetic Resonance in Medicine,  Volume 80, Issue 3, September Pages 1189-1205, 2018.
  99. Maryam Ghahremani, Jaejun Yoo, Sun Ju Chung, Kwangsun Yoo, Jong C. Ye*, and Yong Jeong*. Alteration in the local and global functional connectivity of resting state networks in Parkinson’s disease. J Mov Disord ,11(1): 13-23, 2018.
  100. K. H. Jin and J. C. Ye, "Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal," in IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1448-1461, March 2018, doi: 10.1109/TIP.2017.2771471.
  101. Tasawar Abbas, Shujaat Khan, Muhammad Sajid, Abdul Waha and Jong Chul Ye, "Topological sensitivity based far-field detection of elastic inclusions", Results in Physics  vol 8, March 2018, Pages 442-460
  102. Joowon Lim,  Abdul Wahab, Gwangsik Park, Kyeoreh Lee, Yongkeun Park and Jong Chul Ye, " Beyond Born-Rytov limit for super-resolution optical diffraction tomography", Optics Express, Vol. 25 (24), pp. 30445-30458, 2017.
  103. Eunhee Kang, Junhong Min and Jong Chul Ye, " A Deep Convolutional Neural Network using Directional Wavelets for Low-dose X-ray CT Reconstruction", Medical Physics 44, no. 10 (2017): e360-e375. October, 2017.
  104. ​Kyong Hwan Jin, Ji-Yong Um, Dongwook Lee, Juyoung Lee, Sung-Hong Park and Jong Chul Ye,  " MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based Hankel matrix", Magnetic Resonance in Medicine 78, no. 1 (2017): 327-340.
  105. Jawook Gu, Woong Bae, and Jong Chul Ye, "Translational Motion Correction Algorithm for Truncated Cone-Beam CT using Opposite Projections", Journal of X-ray Science and Technology 2017 Jun 3. doi: 10.3233/XST-16231
  106. Jaejun Yoo, Younghoon Jung, Mikyoung Lim, Jong Chul Ye, and Abdul Wahab, "A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media", SIAM Journal on Imaging Sciences,  10 (3), 1104-1138, 2017
  107. Tasawar Abbas and Habib Ammari and Guanghui Hu and Abdul Wahab and Jong Chul Ye, "Two-Dimensional  Elastic Scattering Coefficients and Enhancement of Nearly Elastic Cloaking", Journal of Elasticity, January, 2017 (online:doi:10.1007/s10659-017-9624-7)
  108. Jong Chul Ye, Jong Min Kim, Kyong Hwan Jin and Kiryung Lee, "Compressive sampling using annihilating filter-based low-rank interpolation",  IEEE Trans. on Information Theory, vol. 63, no. 2, pp.777-801, Feb. 2017.
  109. Sohail Sabir, Changhwan Kim, Sanghoon Cho, Duchang Heo, Kee Hyun Kim, Jong Chul Ye, Seungryong Cho, "Sampling scheme optimization for diffuse optical tomography based on data and image space rankings", J. Biomed. Opt.,  21.10 (2016): 106004-106004
  110. Kyong Hwan Jin, Dongwook Lee, and Jong Chul Ye. "A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank hankel matrix,"  IEEE Trans. on Computational Imaging, vol 2, no. 4, pp. 480 - 495, Dec.  2016.
  111. Jaejun Yoo, Eun Young Kim, Yong Min Ahn, Jong Chul Ye," Topological Persistence Vineyard Approach for Dynamic Functional Brain Connectivity during Resting and Gaming Stages", Journal of Neurscience Methods, vol. 267, pp. 1-12, 2016.
  112. Paul Kyu Han, Jong Chul Ye, Eung Yeop Kim, Seung Hong Choi, and Sung-Hong Park, "Whole Brain Perfusion Imaging with Balanced Steady-State Free Precession Arterial Spin Labeling",  NMR in Biomedicine,  2016 Mar 1;29(3):264-74.
  113. Juyoung Lee, Kyong Hwan Jin, and Jong Chul Ye, "Reference-free single-pass EPI Nyquist ghost correction using annihilating filter-based low rank Hankel  matrix (ALOHA)", Magnetic Resonance in Medicine, Dec 1;76(6):1775-89.
  114. Dongwook Lee,, Kyong Hwan Jin, Eung Yeop Kim, Sung-Hong Park and Jong Chul Ye, "Acceleration of MR parameter mapping using annihilating filter-based low rank Hankel matrix (ALOHA)", Magnetic Resonance in Medicine, 2016 Dec 1;76(6):1848-64.
  115. Young-Beom Lee, Jeonghyeon Lee, Sungho Tak, Kangjoo Lee, Duk L. Na, Sangwon Seo, Yong Jeong, and Jong Chul Ye, "Sparse SPM: Group sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis", NeuroImage,  vol 125,  15 January 2016, Pages 1032–1045 
  116. Jong Chul Ye, Jong Min Kim, and Yoram Bresler, "Improving M-SBL for joint sparse recovery using a subspace penalty", IEEE Trans. on Signal Processing, 2015 Dec 15;63(24):6595-605
  117. Kyong Hwan Jin and Jong Chul Ye, "Annihilating filter based low rank Hankel matrix approach for image inpainting",  IEEE Trans. Image Processing, 2015 Nov;24(11):3498-511.
  118. Minji Lee, Yoseob Han, John Paul Ward, Michael Unser, and Jong Chul Ye, "Interior tomography using 1D generalized total variation -- Part II: multiscale implementation",  SIAM Journal on Imaging Sciences,  2015 Oct 27;8(4):2452-86.
  119. Kyungsang Kim, Taewon Lee, Younghun Seong, Jongha Lee, Kwang Eun Jang, Jaegu Choi, Young Wook Choi, Hak Hee Kim, Hee Jung Shin, Joo Hee Cha, Seungryong Cho and Jong Chul Ye, "Fully Iterative Scatter Corrected Digital Breast Tomosynthesis using GPU-based Fast Monte Carlo Simulation and Composition Ratio Update",  Medical Physics, 2015 Sep 1;42(9):5342-55.
  120. Okkyun Lee, Sungho Tak, and Jong Chul Ye, "A Unified Sparse Recovery and Inference Framework for Functional Diffuse Optical Tomography using Random Effect Model',  IEEE Trans. on Medical Imaging, 2015 Jul;34(7):1602-15.
  121. JooWon Lim, KyeoReh Lee, Kyong Hwan Jin, Seungwoo Shin, SeoEun Lee, YongKeun Park,and Jong Chul Ye, "Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography", Optics Express, 2015 Jun 29;23(13):16933-48.
  122. Ok Kyun Lee, Hyeonbae Kang, Jong Chul Ye, Mikyoung Lim,  "A non-iterative method for the electrical impedance tomography based on joint sparse recovery", Inverse Problems 2015 May 19;31(7):075002
  123. Dae-Su Yee,  Kyong Hwan Jin,  Ji Sang Yahng,  Ho-Soon Yang, Chi Yup Kim, and Jong Chul Ye, "High-speed terahertz reflection threedimensional imaging using beam steering",  Optics Express. 2015 Feb 23;23(4):5027-34.
  124. Kyungsang Kim, Young Don Son, Yoram Bresler, Zang Hee Cho, Jong Beom Ra, and Jong Chul Ye,"Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis", Physics in Medicine and Biology, 2015 Feb 12;60(5):2019.
  125. Kyungsang Kim, Jong Chul Ye, William Worstell, Jinsong Ouyang, Yothin Rakvongthai, Georges El Fakhri and Quanzheng, Li, "Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty", IEEE Trans. on Medical Imaging vol 34, no.3, pp. 748-760, 2015.
  126. John Paul Ward, Minji Lee,  Jong Chul Ye, and Michael Unser, "Interior Tomography using 1D Generalized Total Variation -- Part I: Mathematical Foundation", SIAM Journal on Imaging Sciences,  2015 Jan 22;8(1):226-47.
  127. Paul Kyu Han, Sung-Hong Park, Seong G. Kim and Jong Chul Ye, "Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T", BioMed Research International, 2015 Aug 27;2015.
  128. Junhong Min, Seamus J. Holden, Lina Carlini, Michael Unser, Suliana Manley,and Jong Chul Ye, "3D high-density localization microscopy using hybrid astigmatic/ biplane imaging and sparse image reconstruction"  Biomedical Optics Express, Vol. 5, Issue 11, pp. 3935-3948, 2014. 
  129. Arshi Khalid, Byung Sun Kim, Moo K. Chung, Jong Chul Ye, Daejong Jeon,"Tracing the evolution of multi-scale functional networks in a mouse model of depression using persistent brain network homology", NeuroImage, 101 (2014): 351-363.
  130. Huisu Yoon, Kyung Sang Kim, Daniel Kim, Yoram Bresler, and Jong Chul Ye "Motion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI",  IEEE Trans. Medical Imaging,   Vol. 33, No. 11, pp.2069-2085, Nov. 2014.
  131. Junhong Min, Cedric Vonesch, Hagai Kirshner, Lina Carlini, Nicolas Olivier, Seamus Holden, Suliana Manley, Jong Chul Ye, Michael Unser, "FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data," Scientific Reports 4 , Article no 4577,  Apr. 2014. 
  132. Xiaopeng Zong, Juyoung Lee, Alexander John Poplawsky, Seong-Gi Kim, Jong Chul Ye , "Compressed sensing fMRI using gradient-recalled echo and EPI sequences ," NeuroImage 92 (2014): 312-321.
  133. Kyung Sang Kim, Young Don Son, Zang Hee Cho, Jong Beom Ra, Jong Chul Ye , "Ultra-Fast Hybrid CPU-GPU Multiple Scatter Simulation for 3D PET ," IEEE Journal of Biomedical and Health Informatics, vol. 18 , No. 1 , pp. 148-156 , 2014.01.
  134. Kyoohyun Kim, Kyung Sang Kim, HyunJoo Park, Jong Chul Ye, YongKeun Park , "Real-time visualization of 3-D dynamic microscopic objects using optical diffraction tomography ," Optics Express, vol. 21 , No. 26 , pp. 32269-32278 , 2013.12.
  135. Okkyun Lee, Jong Chul Ye , " Joint sparsity-driven non-iterative simultaneous reconstruction of absorption and scattering in diffuse optical tomography ," Optics Express, vol. 21 , No. 22 , pp. 26589-26604 , 2013.11.
  136. Jiyoung Choi, Dong-Goo Kang, Sunghoon Kang, Younghun Sung, Jong Chul Ye , " A unified statistical framework for material decomposition using multienergy photon counting x-ray detectors ," Medical Physics, vol. 40 , No. 9 , pp. , 2013.09.
  137. Jong Min Kim, Jong Chul Ye , " Corrections to Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing ," IEEE Transactions on Information Theory, vol. 59 , No. 9 , pp. 6148-6149 , 2013.09.
  138. Junhong Min, Jaeduck Jang, Dongmin Keum, Seung-Wook Ryu, Chulhee Choi, Ki-Hun Jeong, Jong Chul Ye " Fluorescent microscopy beyond diffraction limits using speckle illumination and joint support recovery ," Scientific Reports, vol. 3 , No. 2075,  , 2013.06.
  139. Sungho Tak, Jong Chul Ye , " Statistical analysis of fNIRS data: A comprehensive review ," Neuroimage , vol. 85 , No. 15 , pp. 72-91 , 2013.06.
  140. Hyoung Suk Park, Jae Kyu Choi, Kyung-Ran Park, Kyung  Sang Kim, Sang-Hwy Lee, Jong Chul Ye, Jin Keun Seo , " Metal artifact reduction in CT by identifying missing data hidden in metals ," Journal of X-ray Science and Technology, vol. 21 , No. 3 , pp. 357-372 , 2013.00.
  141. Kyong Hwan Jin, Young-Gil Kim, Seung Hyun Cho, Jong Chul Ye, Dae-Su. Yee , " High-speed terahertz reflection three-dimensional imaging for nondestructive evaluation ," Optics Express, vol. 20 , No. 23 , pp. 25432-25440 , 2012.11.
  142. Jong Min Kim, Ok Kyun Lee, Jong Chul Ye , " Improving Noise Robustness in Subspace-Based Joint Sparse Recovery ," IEEE Transactions on Signal processing, vol. 60 , No. 11 , pp. 5799-5809 , 2012.11.
  143. Minwoo Yi, Hyosub Kim, Kyong Hwan Jin, Jong Chul Ye, Jaewook Ahn , " Terahertz substance imaging by waveform shaping ," Optics Express, vol. 20 , No. 18 , pp. 20783-20789 , 2012.08.
  144. Jin Wook Jung, Ok Kyun Lee, Jong Chul Ye , " Source localization approach for functional DOT using MUSIC and FDR control ," Optics Express, vol. 20 , No. 6 , pp. 6267-6285 , 2012.03.
  145. Sang-Gil Park, Kyong Hwan Jin, Minwoo Yi, Jong Chul Ye, Jaewook Ahn, Ki-Hun. Jeong , " Enhancement of Terahertz Pulse Emission by Optical Nanoantenna ,"ACS NANO, vol. 6 , No. 3 , pp. 2026-2031 , 2012.03.
  146. Hua Li, Sungho Tak, Jong Chul Ye , " Lipschitz-Killing curvature based expected Euler characteristics for p-value correction in fNIRS ," Journal of Neuroscience Methods, vol. 204 , No. 1 , pp. 61-67 , 2012.02.
  147. Jong Min Kim, Ok Kyun Lee, Jong Chul Ye , " Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing ," IEEE Transactions on Information Theory, vol. 58 , No. 1 , pp. 278-301 , 2012.01.
  148. Kyung Sang Kim, Jong Chul Ye , " Fully 3D iterative scatter-corrected OSEM for HRRT PET using a GPU ," Physics in Medicine and Biology, vol. 56 , No. 15 , pp. 4991-1669 , 2011.08.
  149. Li Feng, Ricardo Otazo, Hong Jung, Jens H. Jensen, Jong Chul Ye, Daniel K. Sodickson, Daniel Kim , " Accelerated Cardiac T2 Mapping using Breath-hold Multiecho Fast Spin-Echo Pulse Sequence with k-t FOCUSS ," Magnetic Resonance in Medicine, vol. 65 , No. 6 , pp. 1661-1669 , 2011.06.
  150. Kangjoo Lee, Sungho Tak, Jong Chul Ye , " A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion ,"IEEE Transactions on Medical Imaging, vol. 30 , No. 5 , pp. 1176-1089 , 2011.05.
  151. Okkyun Lee, Jong Min Kim, Yoram Bresler, Jong Chul Ye , " Compressive Diffuse Optical Tomography: Noniterative Exact Reconstruction Using Joint Sparsity ," IEEE Transactions on Medical Imaging, vol. 30 , No. 5 , pp. 1129-1142 , 2011.05.
  152. Sungho Tak, Soo Jin Yoon, Jaeduck Jang, Kwangsun Yoo, Yong Jeong, Jong Chul Ye , " Quantitative analysis of hemodynamic and metabolic changes in subcortical vascular dementia using simultaneous near-infrared spectroscopy and fMRI measurements ," Neuroimage, vol. 55 , No. 1 , pp. 176-184 , 2011.03.
  153. Youngchan Kim, Kyung Hwan Jin, Jong Chul Ye, Jaewook Ahn, Dae-Su Yee , " Wavelet Power Spectrum Estimation for High-resolution Terahertz Time-domain Spectroscopy ," Journal of the Optical Society of Korea, vol. 15 , No. 1 , pp. 103-108 , 2011.03.
  154. Jiyoung Choi, Kyung Sang Kim, Min Woo Kim, Won Seong, Jong Chul Ye , " Sparsity driven metal part reconstruction for artifact removal in dental CT ,"Journal of X-ray Science and Technology, vol. 19 , No. 4 , pp. 457-475 , 2011.00.
  155. Sungho Tak, Jaeduck Jang, Kangjoo Lee, Jong Chul Ye , " Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements ," Physics in Medicine and Biology, vol. 55 , No. 11 , pp. 3249-3269 , 2010.06.
  156. Jaeduck Jang, Chae Yun Bae, Je-Kyun Park, Jong Chul Ye , " Self-reference quantitative phase microscopy for microfluidic devices ," Optics Letters, vol. 35 , No. 4 , pp. 514-516 , 2010.02. ( Also selected for publication in the Virtual Journal for Biomedical Optics, vol. 5, iss. 5, March 2010 ) 
  157. Kanghee Lee, Kyung Hwan Jin, Jong Chul Ye , " Coherent optical computing for T-ray imaging ," Optics Letters , vol. 35 , No. 4 , pp. 508-510 , 2010.02.
  158. Hong Jung, Jaeseok Park, Jaeheung Yoo, Jong Chul Ye , " Radial k-t FOCUSS for High-Resolution Cardiac Cine MRI ," Magnetic Resonance in Medicine, vol. 63 , No. , pp. 68-78 , 2010.01.
  159. Hong Jung, Jong Chul Ye , " Motion Estimated and Compensated Compressed Sensing Dynamic Magnetic Resonance Imaging: What We Can Learn From Video Compression Techniques ," International Journal of Imaging Systems and technology, vol. 20 , No. , pp. 81-98 , 2010.00.
  160. Kyung Hwan Jin, Youngchan Kim, Dae-Su. Yee, Ok Kyun Lee, Jong Chul Ye , " Compressed sensing pulse-echo mode terahertz reflectance tomography ," Optics Letters, vol. 34 , No. 24 , pp. 3863-3865 , 2009.12.
  161. Kwang Eun Jang, Sungho Tak, Jinwook Jung, Jaeduck Jang, Yong Jeong, Jong Chul Ye , " Wavelet minimum description length detrending for near-infrared spectroscopy ," Journal of Biomedical optics , vol. 14 , No. , pp. , 2009.05.
  162. Hong Jung, Kyunghyun Sung, Krishna S. Nayak, Eung Yeop Kim, Jong Chul Ye , " k-t FOCUSS: A General Compressed Sensing Framework for High Resolution Dynamic MRI ," Magnetic Resonance in Medicine, vol. 61 , No. 1 , pp. 103-116 , 2009.01.
  163. Jong Chul Ye, Sungho Tak, Kwang Eun Jang, Jinwook Jung, Jaeduck Jang , " NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy ,"Neuroimage, vol. 44 , No. 2 , pp. 428-447 , 2009.01.
  164. Jong Chul Ye, " Compressed sensing shape estimation of star-shaped objects in Fourier imaging ," IEEE Signal Processing Letters, vol. 14 , No. , pp. 750-753 , 2007.10.
  165. Hong Jung, Jong Chul Ye, Eung Yeop Kim , " Improved k-t BLAST and k-t SENSE using FOCUSS ," Physics in Medicine and Biology, vol. 52 , No. , pp. 3201-3226 , 2007.06.
  166. Kwang Eun Jang, Jong Chul Ye , " Single channel blind image deconvolution from radially symmetric blur kernels ," Optics Express, vol. 15 , No. , pp. 3791-3803 , 2007.04.
  167. Jong Chul Ye, Sungho Tak, Yeji Han, and Hyun Wook Park, "Projection Reconstruction MR Imaging using FOCUSS", Magnetic Resonance in  Medicine, vol. 57, pp. 764-775, April 2007.
  168. Jong Chul Ye, Pierre Moulin, Yoram Bresler , " Asymptotic global confidence regions for 3-D parametric shape estimation in inverse problems ,"IEEE Transactions on Image Processing, vol. 15 , No. , pp. 2904-2919 , 2006.10
  169. Jong Chul Ye, Yoram Bresler, Pierre Moulin , " Cramer-Rao bounds for parametric shape estimation in inverse problems ," IEEE Transactions on Image Processing, vol. 12 , No. 1 , pp. 71-84 , 2003.01.
  170. Jong Chul Ye , " A self-referencing level-set method for image reconstruction from sparse Fourier samples ," International Journal of Computer Vision, vol. 50 , No. 3 , pp. 253-270 , 2002.12.
  171. Jong Chul Ye, Charles A. Bouman, Kevin J. Webb, Rick P. Millane , " Nonlinear multigrid algorithms for Bayesian optical diffusion tomography ," IEEE Transactions on Image Processing, vol. 10 , No. 6 , pp. 909-922 , 2001.06
  172. Jong Chul Ye, Yoram Bresler, Pierre Moulin , " Cramer-Rao bounds for 2-D target shape estimation in nonlinear inverse scattering problems with application to passive radar,"IEEE Transactions on Image Processing , vol. 49 , No. 5 , pp. 771-783 , 2001.05.
  173. Jong Chul Ye, Yoram Bresler, Pierre Moulin , " Asymptotic global confidence regions in parametric shape estimation problems ," IEEE Transactions on Information Theory , vol. 46 , No. 5 , pp. 1881-1895 , 2000.08.
  174. Jong Chul Ye, Kevin J. Webb, Charles A. Bouman, Rick P. Millane , " Optical diffusion tomography by iterative-coordinate-descent optimization in a Bayesian framework ," JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION , vol. 16 , No. 10 , pp. 2400-2413 , 1999.10.
  175. Jong Chul Ye, Kevin J. Webb, Rick P. Millane, Thomas J. Downar , " Modified distorted Born iterative method with an approximate Frechet derivative for optical diffusion tomography ," JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION , vol. 16 , No. 7 , pp. 1814-1826 , 1999.07.
  176. Jong Chul Ye, Rick P. Millane, Kevin J. Webb, Thomas J. Downar , " Importance of the grad(D) term in frequency-resolved optical diffusion imaging,"  Optics Letters , vol. 23 , No. 18 , pp. 1423-1425 , 1998.09