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Jong Chul Ye
Jong Chul Ye
Professor, Chung Moon Soul Mirae Endowed Chair, KAIST
kaist.ac.kr의 이메일 확인됨 - 홈페이지
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Ntire 2017 challenge on single image super-resolution: Methods and results
R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang
Proceedings of the IEEE conference on computer vision and pattern …, 2017
22452017
NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy
JC Ye, S Tak, KE Jang, J Jung, J Jang
Neuroimage 44 (2), 428-447, 2009
12162009
A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
E Kang, J Min, JC Ye
Medical physics 44 (10), e360-e375, 2017
9762017
Deep learning COVID-19 features on CXR using limited training data sets
Y Oh, S Park, JC Ye
IEEE transactions on medical imaging 39 (8), 2688-2700, 2020
9552020
k‐t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI
H Jung, K Sung, KS Nayak, EY Kim, JC Ye
Magnetic Resonance in Medicine: An Official Journal of the International …, 2009
8802009
Diffusionclip: Text-guided diffusion models for robust image manipulation
G Kim, T Kwon, JC Ye
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
7382022
Geometric gan
JH Lim, JC Ye
arXiv preprint arXiv:1705.02894, 2017
6842017
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
Y Han, JC Ye
IEEE transactions on medical imaging 37 (6), 1418-1429, 2018
6642018
Diffusion posterior sampling for general noisy inverse problems
H Chung, J Kim, MT Mccann, ML Klasky, JC Ye
arXiv preprint arXiv:2209.14687, 2022
6312022
Image reconstruction is a new frontier of machine learning
G Wang, JC Ye, K Mueller, JA Fessler
IEEE transactions on medical imaging 37 (6), 1289-1296, 2018
5212018
Statistical analysis of fNIRS data: a comprehensive review
S Tak, JC Ye
Neuroimage 85, 72-91, 2014
5072014
Deep learning for tomographic image reconstruction
G Wang, JC Ye, B De Man
Nature machine intelligence 2 (12), 737-748, 2020
5052020
Deep convolutional framelets: A general deep learning framework for inverse problems
JC Ye, Y Han, E Cha
SIAM Journal on Imaging Sciences 11 (2), 991-1048, 2018
4002018
Deep residual learning for accelerated MRI using magnitude and phase networks
D Lee, J Yoo, S Tak, JC Ye
IEEE Transactions on Biomedical Engineering 65 (9), 1985-1995, 2018
3832018
Score-based diffusion models for accelerated MRI
H Chung, JC Ye
arXiv preprint arXiv:2110.05243, 2021
3762021
Improving diffusion models for inverse problems using manifold constraints
H Chung, B Sim, D Ryu, JC Ye
Advances in Neural Information Processing Systems 35, 25683-25696, 2022
3702022
Deep learning with domain adaptation for accelerated projection‐reconstruction MR
Y Han, J Yoo, HH Kim, HJ Shin, K Sung, JC Ye
Magnetic resonance in medicine 80 (3), 1189-1205, 2018
3622018
-Space Deep Learning for Accelerated MRI
Y Han, L Sunwoo, JC Ye
IEEE transactions on medical imaging 39 (2), 377-386, 2019
3612019
Deep convolutional framelet denosing for low-dose ct via wavelet residual network
E Kang, W Chang, J Yoo, JC Ye
IEEE transactions on medical imaging 37 (6), 1358-1369, 2018
3532018
Improved k–t BLAST and k–t SENSE using FOCUSS
H Jung, JC Ye, EY Kim
Physics in Medicine & Biology 52 (11), 3201, 2007
3412007
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학술자료 1–20