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유재준
Yoo, Jaejun

유재준

Graduate School of Artificial Intelligence(인공지능대학원)

  • Lab. of Advanced Imaging Technology
  • Website
Research Interests
AI;Computer Vision;Medical Image Processing;Machine Learning;인공지능;컴퓨터 비전;의료영상처리;머신러닝
Lab Description
Our main research area lies at the intersection of computer vision, machine learning, and inverse problems, including natural image recovery and medical imaging. We have a strong interest in generative models, representation learning, and the use of signal processing theories for image processing. Our goal is to build strong and intelligent signal processing models, capable of recreating the world we perceive. We aim to establish a bridge between signal processing and deep learning, taking the best of both worlds. We study deep learning models that learn structural priors by synthesizing and modeling millions of images and videos. Along the way, the learned models provide insights to seek mathematical elegance and a clear understanding of our world, which in turn encourages us to find better models to analyze signals in nature.
Researcher ID Research Gate ORCiD Scopus Google Scholar
Previous
Data Augmentation for Low-Level Vision: CutBlur and Mixture-of-Augmentation File

Ahn, Namhyuk , Yoo, Jaejun , Sohn, Kyung-Ah

Article Issue Date2024-01 View18
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-Based Image Super-Resolution in Remote Sensing File

Min, Jeongho , Lee, Yejun , Kim, Dongyoung , Yoo, Jaejun

Article Issue Date2024-01 View13
Dynamic Fourier ptychography with deep spatiotemporal priors File File

Bohra, Pakshal , Pham, Thanh-an , Long, Yuxuan , Yoo, Jaejun , Unser, Michael

Article Issue Date2023-06 View6
GAN 기반 의료영상 생성 모델에 대한 품질 및 다양성 평가 및 분석 File

장유진 , 유재준 , 홍헬렌

Article Issue Date2022-07 View16
Time-Dependent Deep Image Prior for Dynamic MRI File

Yoo, Jaejun , Jin, Kyong Hwan , Gupta, Harshit , Yerly, Jerome , Stuber, Matthias , Unser, Michael

Article Issue Date2021-12 View8
Deep Learning Diffuse Optical Tomography File

Yoo, Jaejun , Sabir, Sohail , Heo, Duchang , Kim, Kee Hyun , Wahab, Abdul , Choi, Yoonseok , Lee, Seul-, I , Chae, Eun Young , Kim, Hak Hee , Bae, Young Min , et al

Article Issue Date2020-04 View8
A MATHEMATICAL FRAMEWORK FOR DEEP LEARNING IN ELASTIC SOURCE IMAGING File

Yoo, Jaejun , Wahab, Abdul , Ye, Jong Chul

Article Issue Date2018-10 View8
Deep learning with domain adaptation for accelerated projection-reconstruction MR File

Han, Yoseob , Yoo, Jaejun , Kim, Hak Hee , Shin, Hee Jung , Sung, Kyunghyun , Ye, Jong Chul

Article Issue Date2018-09 View8
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks File

Lee, Dongwook , Yoo, Jaejun , Tak, Sungho , Ye, Jong Chul

Article Issue Date2018-09 View8
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network File

Kang, Eunhee , Chang, Won , Yoo, Jaejun , Ye, Jong Chul

Article Issue Date2018-06 View9
Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson's Disease File

Ghahremani, Maryam , Yoo, Jaejun , Chung, Sun Ju , Yoo, Kwangsun , Ye, Jong C. , Jeong, Yong

Article Issue Date2018-01 View6
A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media File

Yoo, Jaejun , Jung, Younghoon , Lim, Mikyoung , Ye, Jong Chul , Wahab, Abdul

Article Issue Date2017-08 View8
Topological persistence vineyard for dynamic functional brain connectivity during resting and gaming stages File

Yoo, Jaejun , Kim, Eun Young , Ahn, Yong Min , Ye, Jong Chul

Article Issue Date2016-07 View7
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