Jaihyun Park - IEEE Xplore Author Profile

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Author details

Jaihyun Park

Publications

5

Citations

151

Publications by Year

20162020

Co-Authors:

Kazutoshi AkitaJaeHyun BaekYifu ChenQiuju DaiYukang Ding

Show All Co-Authors (67)

Jaihyun Park

Affiliation

Korea University, Seoul, Korea

Publication Topics

Generative Adversarial Networks,
L1 Loss,
Loss Function,
Low-resolution Images,
Residual Block,
Single Image Super-resolution,
Super-resolution,
Bicubic Interpolation,
Convolutional Neural Network,
Feature Maps,
Fine Details,
High-resolution Images

Biography

Jaihyun Park (Student Member, IEEE) received the B.S. degree in digital media from the Catholic University of Korea, South Korea, in 2011. Since 2013, he has been in the integrated M.S. and Ph.D. program in electrical engineering with Korea University, Seoul, South Korea. His research interest is in image enhancement (dehazing and super-resolution), image synthesis, and object detection.(Based on document published on 28 February 2020).

Publications

5

Citations

151

Publications by Year

20162020

Co-Authors:

Kazutoshi Akita
JaeHyun Baek
Yifu Chen
Qiuju Dai
Yukang Ding

Show All Co-Authors (67)

Author's Published Works

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Showing 1-5 of 5 results

Conferences (4)

Journals (1)

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Single Year
Jaihyun Park(5)
Hanseok Ko(4)
Jeongki Min(3)
Kanghyu Lee(3)
Gwantae Kim(3)
Korea University, Seoul, Korea(2)
Army Research Laboratory, Adelphi, MD, USA(2)
School of Electrical Engineering, Korea University, Seoul, South Korea(1)
Dept. of Vision Information Processing, Korea University, Seoul, Korea(1)
Information Science Division, Army Research Laboratory (ARL), Adelphi, USA(1)
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(3)
2016 IEEE International Conference on Consumer Electronics (ICCE)(1)
IEEE Transactions on Image Processing(1)
IEEE(5)
Seattle, WA, USA(3)
Las Vegas, NV, USA(1)
Generative Adversarial Networks(3)
L1 Loss(3)
Loss Function(3)
Low-resolution Images(3)
Residual Block(3)

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Results

FBRNN: feedback recurrent neural network for extreme image super-resolution

Junyeop Lee;Jaihyun Park;Kanghyu Lee;Jeongki Min;Gwantae Kim;Bokyeung Lee;Bonhwa Ku;David K. Han;Hanseok Ko

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Cited by: Papers (9)

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Single image extreme Super Resolution (SR) is a difficult task as scale factor in the order of 10X or greater is typically attempted. For instance, in the case of 16x upscale of an image, a single pixel from a low resolution image gets expanded to a 16x16 image patch. Such attempts often result fuzzy quality and loss in details in reconstructed images. To handle these difficulties, we propose a ne...Show More

FBRNN: feedback recurrent neural network for extreme image super-resolution

Junyeop Lee;Jaihyun Park;Kanghyu Lee;Jeongki Min;Gwantae Kim;Bokyeung Lee;Bonhwa Ku;David K. Han;Hanseok Ko

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

Kai Zhang;Shuhang Gu;Radu Timofte;Taizhang Shang;Qiuju Dai;Shengchen Zhu;Tong Yang;Yandong Guo;Younghyun Jo;Sejong Yang;Seon Joo Kim;Lin Zha;Jiande Jiang;Xinbo Gao;Wen Lu;Jing Liu;Kwangjin Yoon;Taegyun Jeon;Kazutoshi Akita;Takeru Ooba;Norimichi Ukita;Zhipeng Luo;Yuehan Yao;Zhenyu Xu;Dongliang He;Wenhao Wu;Yukang Ding;Chao Li;Fu Li;Shilei Wen;Jianwei Li;Fuzhi Yang;Huan Yang;Jianlong Fu;Byung-Hoon Kim;JaeHyun Baek;Jong Chul Ye;Yuchen Fan;Thomas S. Huang;Junyeop Lee;Bokyeung Lee;Jungki Min;Gwantae Kim;Kanghyu Lee;Jaihyun Park;Mykola Mykhailych;Haoyu Zhong;Yukai Shi;Xiaojun Yang;Zhijing Yang;Liang Lin;Tongtong Zhao;Jinjia Peng;Huibing Wang;Zhi Jin;Jiahao Wu;Yifu Chen;Chenming Shang;Huanrong Zhang;Jeongki Min;P.S. Hrishikesh;Densen Puthussery;C.V. Jiji

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Cited by: Papers (32)

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This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best percep...Show More

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

Kai Zhang;Shuhang Gu;Radu Timofte;Taizhang Shang;Qiuju Dai;Shengchen Zhu;Tong Yang;Yandong Guo;Younghyun Jo;Sejong Yang;Seon Joo Kim;Lin Zha;Jiande Jiang;Xinbo Gao;Wen Lu;Jing Liu;Kwangjin Yoon;Taegyun Jeon;Kazutoshi Akita;Takeru Ooba;Norimichi Ukita;Zhipeng Luo;Yuehan Yao;Zhenyu Xu;Dongliang He;Wenhao Wu;Yukang Ding;Chao Li;Fu Li;Shilei Wen;Jianwei Li;Fuzhi Yang;Huan Yang;Jianlong Fu;Byung-Hoon Kim;JaeHyun Baek;Jong Chul Ye;Yuchen Fan;Thomas S. Huang;Junyeop Lee;Bokyeung Lee;Jungki Min;Gwantae Kim;Kanghyu Lee;Jaihyun Park;Mykola Mykhailych;Haoyu Zhong;Yukai Shi;Xiaojun Yang;Zhijing Yang;Liang Lin;Tongtong Zhao;Jinjia Peng;Huibing Wang;Zhi Jin;Jiahao Wu;Yifu Chen;Chenming Shang;Huanrong Zhang;Jeongki Min;P.S. Hrishikesh;Densen Puthussery;C.V. Jiji

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Unsupervised Real-World Super Resolution with Cycle Generative Adversarial Network and Domain Discriminator

Gwantae Kim;Jaihyun Park;Kanghyu Lee;Junyeop Lee;Jeongki Min;Bokyeung Lee;David K. Han;Hanseok Ko

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Cited by: Papers (37)

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This paper proposes an unsupervised single-image Super-Resolution(SR) model using cycleGAN and domain discriminator to solve the problem of SR with unknown degradation using unpaired dataset. In previous approaches, paired dataset is required for training with assumed levels of image degradation. In real world SR applications, however, training sets are typically not of low and high resolution ima...Show More

Unsupervised Real-World Super Resolution with Cycle Generative Adversarial Network and Domain Discriminator

Gwantae Kim;Jaihyun Park;Kanghyu Lee;Junyeop Lee;Jeongki Min;Bokyeung Lee;David K. Han;Hanseok Ko

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2020 | Conference Paper |

Fusion of Heterogeneous Adversarial Networks for Single Image Dehazing

Jaihyun Park;David K. Han;Hanseok Ko

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Cited by: Papers (69)

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In this paper, we propose a novel image dehazing method. Typical deep learning models for dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be effective for indoor image dehazing but less so for outdoor images. We propose a heterogeneous Generative Adversarial Networks (GAN) based method composed of a cycle-consistent Generative Adversarial Networks (CycleGAN) fo...Show More

Fusion of Heterogeneous Adversarial Networks for Single Image Dehazing

Jaihyun Park;David K. Han;Hanseok Ko

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

SVM based dynamic classifier for sleep disorder monitoring wearable device

Jaihyun Park;Daehun Kim;Cheoljong Yang;Hanseok Ko

2016 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2016 | Conference Paper |

Cited by: Papers (4)

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An accelerometer embedded wrist-worn device is widely used for sleep assessment. However, conventional methods determine a state of user to "sleep" or "wakefulness" according to whether the accelerometer value of individual epoch exceeds a certain threshold or not. As a result, high miss-classification rate is observed due to user's small intermittent movements while sleeping and short term moveme...Show More

SVM based dynamic classifier for sleep disorder monitoring wearable device

Jaihyun Park;Daehun Kim;Cheoljong Yang;Hanseok Ko

2016 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2016 | Conference Paper |

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