Search Results - "Yoo, Jaejun"

Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks by Lee, Dongwook, Yoo, Jaejun, Tak, Sungho, Ye, Jong Chul

“…Objective: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging…”
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Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network by Kang, Eunhee, Chang, Won, Yoo, Jaejun, Ye, Jong Chul

“…Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently…”
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Deep residual learning for compressed sensing MRI by Dongwook Lee, Jaejun Yoo, Jong Chul Ye

“…Compressed sensing (CS) enables significant reduction of MR acquisition time with performance guarantee. However, computational complexity of CS is usually…”
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Photorealistic Style Transfer via Wavelet Transforms by Yoo, Jaejun, Uh, Youngjung, Chun, Sanghyuk, Kang, Byeongkyu, Ha, Jung-Woo

“…Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by…”
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Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy by Yoo, Jaejun, Ahn, Namhyuk, Sohn, Kyung-Ah

“…Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision…”
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Rethinking the Truly Unsupervised Image-to-Image Translation by Baek, Kyungjune, Choi, Yunjey, Uh, Youngjung, Yoo, Jaejun, Shim, Hyunjung

“…Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision…”
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Enhancing Optical Camera Communication Performance for Collaborative Communication Using Positioning Information by Utama, Ida Bagus Krishna Yoga, Sitanggang, Ones Sanjerico, Nasution, Muhammad Rangga Aziz, Joha, Md. Ibne, Yoo, Jaejun, Jang, Yeong Min

“…Optical camera communication (OCC) has emerged as a promising alternative technology for radio frequency (RF)-based communication systems. However, existing…”
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Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification by Woong Bae, Jaejun Yoo, Jong Chul Ye

“…The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an…”
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NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results by Lugmayr, Andreas, Danelljan, Martin, Timofte, Radu, Ahn, Namhyuk, Bai, Dongwoon, Cai, Jie, Cao, Yun, Chen, Junyang, Cheng, Kaihua, Chun, SeYoung, Deng, Wei, El-Khamy, Mostafa, Ho, Chiu Man, Ji, Xiaozhong, Kheradmand, Amin, Kim, Gwantae, Ko, Hanseok, Lee, Kanghyu, Lee, Jungwon, Li, Hao, Liu, Ziluan, Liu, Zhi-Song, Liu, Shuai, Lu, Yunhua, Meng, Zibo, Michelini, Pablo Navarrete, Micheloni, Christian, Prajapati, Kalpesh, Ren, Haoyu, Seo, Yong Hyeok, Siu, Wan-Chi, Sohn, Kyung-Ah, Tai, Ying, Umer, Rao Muhammad, Wang, Shuangquan, Wang, Huibing, Wu, Timothy Haoning, Wu, Haoning, Yang, Biao, Yang, Fuzhi, Yoo, Jaejun, Zhao, Tongtong, Zhou, Yuanbo, Zhuo, Haijie, Zong, Ziyao, Zou, Xueyi

“…This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses…”
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Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson’s Disease by Ghahremani, Maryam, Yoo, Jaejun, Chung, Sun Ju, Yoo, Kwangsun, Ye, Jong C., Jeong, Yong

“…Objective Parkinson’s disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients’ motor function, as well as of cognition, as…”
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NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results by Timofte, Radu, Agustsson, Eirikur, Van Gool, Luc, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, Xintao Wang, Yapeng Tian, Ke Yu, Yulun Zhang, Shixiang Wu, Chao Dong, Liang Lin, Yu Qiao, Loy, Chen Change, Woong Bae, Jaejun Yoo, Yoseob Han, Jong Chul Ye, Jae-Seok Choi, Munchurl Kim, Yuchen Fan, Jiahui Yu, Wei Han, Ding Liu, Haichao Yu, Zhangyang Wang, Honghui Shi, Xinchao Wang, Huang, Thomas S., Yunjin Chen, Kai Zhang, Wangmeng Zuo, Zhimin Tang, Linkai Luo, Shaohui Li, Min Fu, Lei Cao, Wen Heng, Giang Bui, Truc Le, Ye Duan, Dacheng Tao, Ruxin Wang, Xu Lin, Jianxin Pang, Jinchang Xu, Yu Zhao, Xiangyu Xu, Jinshan Pan, Deqing Sun, Yujin Zhang, Xibin Song, Yuchao Dai, Xueying Qin, Xuan-Phung Huynh, Tiantong Guo, Mousavi, Hojjat Seyed, Tiep Huu Vu, Monga, Vishal, Cruz, Cristovao, Egiazarian, Karen, Katkovnik, Vladimir, Mehta, Rakesh, Jain, Arnav Kumar, Agarwalla, Abhinav, Sai Praveen, C. V., Ruofan Zhou, Hongdiao Wen, Che Zhu, Zhiqiang Xia, Zhengtao Wang, Qi Guo

“…This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed…”
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StarGAN v2: Diverse Image Synthesis for Multiple Domains by Choi, Yunjey, Uh, Youngjung, Yoo, Jaejun, Ha, Jung-Woo

“…A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of…”
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Deep learning with domain adaptation for accelerated projection‐reconstruction MR by Han, Yoseob, Yoo, Jaejun, Kim, Hak Hee, Shin, Hee Jung, Sung, Kyunghyun, Ye, Jong Chul

“…Purpose The radial k‐space trajectory is a well‐established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial…”
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Time-Dependent Deep Image Prior for Dynamic MRI by Yoo, Jaejun, Jin, Kyong Hwan, Gupta, Harshit, Yerly, Jerome, Stuber, Matthias, Unser, Michael

“…We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data…”
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Data Augmentation for Low-Level Vision: CutBlur and Mixture-of-Augmentation by Ahn, Namhyuk, Yoo, Jaejun, Sohn, Kyung-Ah

“…Data augmentation (DA) is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level…”
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Bridging the Domain Gap: A Simple Domain Matching Method for Reference-Based Image Super-Resolution in Remote Sensing by Min, Jeongho, Lee, Yejun, Kim, Dongyoung, Yoo, Jaejun

“…Recently, reference-based image super-resolution (RefSR) has shown excellent performance in image super-resolution (SR) tasks. The main idea of RefSR is to…”
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Universal Dehazing via Haze Style Transfer by Park, Eunpil, Yoo, Jaejun, Sim, Jae-Young

“…Single image dehazing has been actively studied to overcome the quality degradation of hazy images. Most of the existing methods take model-based approaches…”
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Deep Learning Diffuse Optical Tomography by 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, Choi, Young-Wook, Cho, Seungryong, Ye, Jong Chul

“…Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to…”
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A MATHEMATICAL FRAMEWORK FOR DEEP LEARNING IN ELASTIC SOURCE IMAGING by YOO, JAEJUN, WAHAB, ABDUL, YE, JONG CHUL

“…An inverse elastic source problem with sparse measurements is our concern. A generic mathematical framework is proposed which extends a low-dimensional…”
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Style harmonization of panoramic radiography using deep learning by Kim, Hak-Sun, Seol, Jaejung, Lee, Ji-Yun, Han, Sang-Sun, Yoo, Jaejun, Lee, Chena

“…This study aimed to harmonize panoramic radiograph images from different equipment in a single institution to display similar styles. A total of 15,624…”
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