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Showing results 1 to 36 of 36

1	A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix Jin, Kyong Hwan; Lee, Dong Wook; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.2, no.4, pp.480 - 495, 2016-12
2	A MATHEMATICAL FRAMEWORK FOR DEEP LEARNING IN ELASTIC SOURCE IMAGING Yoo, Jaejun; Wahab, Abdul; Ye, Jong Chul, SIAM JOURNAL ON APPLIED MATHEMATICS, v.78, no.5, pp.2791 - 2818, 2018-11
3	A non-iterative method for the electrical impedance tomography based on joint sparse recovery Lee, Ok Kyun; Kang, Hyeonbae; Ye, Jong Chul; Lim, Mikyoung, INVERSE PROBLEMS, v.31, no.7, 2015-07
4	Acceleration of MR Parameter Mapping Using Annihilating Filter-Based Low Rank Hankel Matrix (ALOHA) Lee, Dong Wook; Jin, Kyong Hwan; Kim, Eung Yeop; Park, Sung-Hong; Ye, Jong Chul, MAGNETIC RESONANCE IN MEDICINE, v.76, no.6, pp.1848 - 1864, 2016-12
5	Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound Khan, Shujaat; Huh, Jaeyoung; Ye, Jong Chul, IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL, v.67, no.8, pp.1558 - 1572, 2020-08
6	Beyond Born-Rytov limit for super-resolution optical diffraction tomography Lim, JooWon; Wahab, Abdul; Park, GwangSik; Lee, Kyeo Reh; Park, Yong Keun; Ye, Jong Chul, OPTICS EXPRESS, v.25, no.24, pp.30445 - 30458, 2017-11
7	Compressed sensing fMRI using gradient-recalled echo and EPI sequences Zong, Xiaopeng; Lee, Juyoung; Poplawsky, Alexander John; Kim, Seong-Gi; Ye, Jong Chul, NEUROIMAGE, v.92, pp.312 - 321, 2014-05
8	Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T Han, Paul Kyu; Park, Sung-Hong; Kim, Seong-Gi; Ye, Jong Chul, BIOMED RESEARCH INTERNATIONAL, v.2015, pp.131926, 2015-08
9	Compressed Sensing via Measurement-Conditional Generative Models Kim, Kyung-Su; Lee, Jung Hyun; Yang, Eunho, IEEE ACCESS, v.9, pp.155335 - 155352, 2021-11
10	Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing Kim, Jong Min; Lee, Ok Kyun; Ye, Jong Chul, IEEE TRANSACTIONS ON INFORMATION THEORY, v.58, no.1, pp.278 - 301, 2012-01
11	Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation Ye, Jong Chul; Kim, Jong Min; Jin, Kyong Hwan; Lee, Kiryung, IEEE TRANSACTIONS ON INFORMATION THEORY, v.63, no.2, pp.777 - 801, 2017-02
12	Cramer-Rao bounds for parametric shape estimation in inverse problems Ye, Jong Chul; Bresler, Y; Moulin, P, IEEE TRANSACTIONS ON IMAGE PROCESSING, v.12, no.1, pp.71 - 84, 2003-01
13	Cycle-consistent adversarial denoising network for multiphase coronary CT angiography Kang, Eunhee; Koo, Hyun Jung; Yang, Dong Hyun; Seo, Joon Bum; Ye, Jong Chul, MEDICAL PHYSICS, v.46, no.2, pp.550 - 562, 2019-02
14	Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems Ye, Jong Chul; Han, Yoseob; Cha, Eunju, SIAM JOURNAL ON IMAGING SCIENCES, v.11, no.2, pp.991 - 1048, 2018-07
15	Deep Learning Diffuse Optical Tomography Yoo, Jaejun; Sabir, Sohail; Heo, Duchang; Kim, Kee Hyun; Wahab, Abdul; Choi, Yoonseok; Lee, Seul-, I; et al, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.39, no.4, pp.877 - 887, 2020-04
16	Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy Park, Hyoungjun; Na, Myeongsu; Kim, Bumju; Park, Soohyun; Kim, Ki Hean; Chang, Sunghoe; Ye, Jong Chul, NATURE COMMUNICATIONS, v.13, no.1, 2022-06
17	Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks Lee, Dongwook; Yoo, Jaejun; Tak, Sungho; Ye, Jong Chul, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.65, no.9, pp.1985 - 1995, 2018-09
18	DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning Ryu, DongHun; Ryu, Dongmin; Baek, YoonSeok; Cho, Hyungjoo; Kim, Geon; Kim, Young Seo; Lee, Yongki; et al, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.5, pp.1508 - 1518, 2021-05
19	Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications Zhao, Zhizhen; Ye, Jong Chul; Bresler, Yoram, IEEE SIGNAL PROCESSING MAGAZINE, v.40, no.1, pp.148 - 163, 2023-01
20	Importance of the del D term in frequency-resolved optical diffusion imaging Ye, Jong Chul; Millane, RP; Webb, KJ; Downar, TJ, OPTICS LETTERS, v.23, no.18, pp.1423 - 1425, 1998-09
21	k-Space deep learning for reference-free EPI ghost correction Lee, Juyoung; Han, Yoseob; Ryu, Jae-Kyun; Park, Jang-Yeon; Ye, Jong Chul, MAGNETIC RESONANCE IN MEDICINE, v.82, no.6, pp.2299 - 2313, 2019-12
22	Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning Cha, Eunju; Chung, Hyungjin; Jang, Jaeduck; Lee, Junho; Lee, Eunha; Ye, Jong Chul, ACS NANO, v.16, no.7, pp.10314 - 10326, 2022-06
23	Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain Chung, Hyungjin; Huh, Jaeyoung; Kim, Geon; Park, Yong Keun; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.747 - 758, 2021-07
24	Radial k-t FOCUSS for High-Resolution Cardiac Cine MRI Jung, Hong; Park, Jae-Seok; Yoo, Jae-Heung; Ye, Jong-Chul, MAGNETIC RESONANCE IN MEDICINE, v.63, pp.68 - 78, 2010-01
25	Sampling scheme optimization for diffuse optical tomography based on data and image space rankings Sabir, Sohail; Kim, Changhwan; Cho, Sanghoon; Heo, Duchang; Kim, Kee Hyun; Ye, Jong Chul; Cho, Seungryong, JOURNAL OF BIOMEDICAL OPTICS, v.21, no.10, 2016-10
26	Score-based diffusion models for accelerated MRI Chung, Hyungjin; Ye, Jong Chul, MEDICAL IMAGE ANALYSIS, v.80, 2022-08
27	Source localization approach for functional DOT using MUSIC and FDR control Jung, Jin-Wook; Lee, Ok-Kyun; Ye, Jong-Chul, OPTICS EXPRESS, v.20, no.6, pp.6267 - 6285, 2012-03
28	Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal Jin, Kyong Hwan; Ye, Jong Chul, IEEE TRANSACTIONS ON IMAGE PROCESSING, v.27, no.3, pp.1448 - 1461, 2018-03
29	Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning Jacob, Mathews; Mani, Merry P.; Ye, Jong Chul, IEEE SIGNAL PROCESSING MAGAZINE, v.37, no.1, pp.54 - 68, 2020-01
30	The Foundations of Computational Imaging: A signal processing perspective Karl, W. Clem; Fowler, James E.; Bouman, Charles A.; Cetin, Mujdat; Wohlberg, Brendt; Ye, Jong Chul, IEEE SIGNAL PROCESSING MAGAZINE, v.40, no.5, pp.40 - 53, 2023-07
31	Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images Choi, Yunsu; Han, Minah; Jang, Hanjoo; Shim, Hyunjung; Baek, Jongduk, PLOS ONE, v.17, no.1, 2022-01
32	Unified Theory for Recovery of Sparse Signals in a General Transform Domain Lee, Kiryung; Li, Yanjun; Jin, Kyong Hwan; Ye, Jong Chul, IEEE TRANSACTIONS ON INFORMATION THEORY, v.64, no.8, pp.5457 - 5477, 2018-08
33	Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN Oh, Gyutaek; Sim, Byeongsu; Chung, HyungJin; Sunwoo, Leonard; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.6, pp.1285 - 1296, 2020-08
34	Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution Cha, Eunju; Chung, Hyungjin; Kim, Eung Yeop; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.1, pp.166 - 179, 2021-01
35	Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization Oh, Gyutaek; Bae, Hyokyoung; Ahn, Hyun-Seo; Park, Sung-Hong; Moon, Won-Jin; Ye, Jong Chul, MEDICAL IMAGE ANALYSIS, v.79, 2022-07
36	Weakly-supervised progressive denoising with unpaired CT images Kim, Byeongjoon; Shim, Hyunjung; Baek, Jongduk, MEDICAL IMAGE ANALYSIS, v.71, 2021-07

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