Garron K. Morris - IEEE Xplore Author Profile

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Author details

Garron K. Morris

Also published under: Garron Morris, G. K. Morris

Publications

15

Citations

49

Publications by Year

20062019

Co-Authors:

Emmanuel ArthurMartin D. BallAvijit BhuniaMichael A. CymermanOndrej Flek

Show All Co-Authors (27)

Garron K. Morris

Also published under: Garron Morris, G. K. Morris

Affiliation

Rockwell Automation

Publication Topics

Circuit Board,
Corrosion Rate,
Electronic Components,
Junction Temperature,
Mixed Gas,
Relative Humidity,
Weight Gain,
3-phase,
Accelerated Testing Methods,
Acceleration Factor,
Air Velocity,
Airflow

Biography

Garron K. Morris,Rockwell Automation,6400 West Enterprise Drive,Mequon, WI 53092, USA,e-mail: gkmorris@ra.rockwell.com, Garron Morris is a Senior Principal Engineer in the motor drives research and development group at Rockwell Automation. He has over 20 years of experience in reliability and thermal management of power electronics. He has a BS and MS in Mechanical Engineering from University of Wisconsin-Milwaukee. Garron is an IEEE and ASQ member. As a Design for Six Sigma Master Black belt, he has taught over 1700 students worldwide. He has authored 25 conference and seven journal publications, and has 15 US patents. Garron is also an ASQ Certified Reliability Engineer. (Based on document published on 25 July 2019).

Publications

15

Citations

49

Publications by Year

20062019

Co-Authors:

Emmanuel Arthur
Martin D. Ball
Avijit Bhunia
Michael A. Cymerman
Ondrej Flek

Show All Co-Authors (27)

Author's Published Works

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Showing 1-17 of 17 results

Conferences (9)

Journals (8)

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Jae-Seok Choi(14)
Munchurl Kim(14)
Soomin Seo(5)
Radu Timofte(4)
Saehun Kim(4)
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea(4)
ESAT, KU Leuven, Belgium(2)
Beijing University of Posts and Telecommunications, China(2)
Xiamen University, China(2)
School of EE, Korea Advanced Institute of Science and Technology, Korea(2)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(4)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
IEEE Access(2)
IEEE Transactions on Image Processing(2)
2015 IEEE International Conference on Image Processing (ICIP)(1)
IEEE(17)
Salt Lake City, UT, USA(4)
Honolulu, HI, USA(2)
Prague, Czech Republic(1)
Quebec City, QC, Canada(1)
Seoul, Korea (South)(1)
Bicubic Interpolation(8)
Convolutional Neural Network(8)
High-resolution Images(7)
Low-resolution Images(7)
Convolutional Layers(6)

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Results

X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments

DongKi Noh;Changki Sung;Teayoung Uhm;WooJu Lee;Hyungtae Lim;Jaeseok Choi;Kyuewang Lee;Dasol Hong;Daeho Um;Inseop Chung;Hochul Shin;MinJung Kim;Hyoung-Rock Kim;SeungMin Baek;Hyun Myung

IEEE Robotics and Automation Letters

Year: 2023 | Volume: 8, Issue: 2 | Journal Article |

Cited by: Papers (3)

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In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that ha...Show More

X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments

DongKi Noh;Changki Sung;Teayoung Uhm;WooJu Lee;Hyungtae Lim;Jaeseok Choi;Kyuewang Lee;Dasol Hong;Daeho Um;Inseop Chung;Hochul Shin;MinJung Kim;Hyoung-Rock Kim;SeungMin Baek;Hyun Myung

IEEE Robotics and Automation Letters

Year: 2023 | Volume: 8, Issue: 2 | Journal Article |

MD3D: Mixture-Density-Based 3D Object Detection in Point Clouds

Jaeseok Choi;Yeji Song;Yerim Kim;Jaeyoung Yoo;Nojun Kwak

Year: 2022 | Volume: 10 | Journal Article |

Cited by: Papers (4)

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The design factors of anchor boxes, such as shape, placement, and target assignment policy, greatly influence the performance and latency of the 3D object detectors. Unlike image-based 2D anchors, 3D anchors must be placed in a 3D space and determined differently for each class of different sizes. This imposes a significant burden on the design complexity. To tackle this issue, various studies hav...Show More

MD3D: Mixture-Density-Based 3D Object Detection in Point Clouds

Jaeseok Choi;Yeji Song;Yerim Kim;Jaeyoung Yoo;Nojun Kwak

Year: 2022 | Volume: 10 | Journal Article |

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Jaeseok Choi;Yeji Song;Nojun Kwak

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2021 | Conference Paper |

Cited by: Papers (26)

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Data augmentation has greatly contributed to improving the performance in image recognition tasks, and a lot of related studies have been conducted. However, data augmentation on 3D point cloud data has not been much explored. 3D label has more sophisticated and rich structural information than the 2D label, so it enables more diverse and effective data augmentation. In this paper, we propose part...Show More

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Jaeseok Choi;Yeji Song;Nojun Kwak

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2021 | Conference Paper |

UPSNet: Unsupervised Pan-Sharpening Network With Registration Learning Between Panchromatic and Multi-Spectral Images

Soomin Seo;Jae-Seok Choi;Jaehyup Lee;Hyun-Ho Kim;Doochun Seo;Jaeheon Jeong;Munchurl Kim

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (27)

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Recent advances in deep learning have shown impressive performances for pan-sharpening. Pan-sharpening is the task of enhancing the spatial resolution of a multi-spectral (MS) image by exploiting the high-frequency information of its corresponding panchromatic (PAN) image. Many deep-learning-based pan-sharpening methods have been developed recently, surpassing the performances of traditional pan-s...Show More

UPSNet: Unsupervised Pan-Sharpening Network With Registration Learning Between Panchromatic and Multi-Spectral Images

Soomin Seo;Jae-Seok Choi;Jaehyup Lee;Hyun-Ho Kim;Doochun Seo;Jaeheon Jeong;Munchurl Kim

Year: 2020 | Volume: 8 | Journal Article |

S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks

Jae-Seok Choi;Yongwoo Kim;Munchurl Kim

IEEE Geoscience and Remote Sensing Letters

Year: 2020 | Volume: 17, Issue: 5 | Journal Article |

Cited by: Papers (15)

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Recently, many deep-learning-based pan-sharpening methods have been proposed for generating high-quality pan-sharpened (PS) satellite images. These methods focused on various types of convolutional neural network (CNN) structures, which were trained by simply minimizing a spectral loss between network outputs and the corresponding high-resolution (HR) multi-spectral (MS) target images. However, ow...Show More

S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks

Jae-Seok Choi;Yongwoo Kim;Munchurl Kim

IEEE Geoscience and Remote Sensing Letters

Year: 2020 | Volume: 17, Issue: 5 | Journal Article |

AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results

Andrey Ignatov;Jagruti Patel;Radu Timofte;Bolun Zheng;Xin Ye;Li Huang;Xiang Tian;Saikat Dutta;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;A. N. Rajagopalan;Zhiwei Xiong;Jie Huang;Guanting Dong;Mingde Yao;Dong Liu;Ming Hong;Wenying Lin;Yanyun Qu;Jae-Seok Choi;Woonsung Park;Munchurl Kim;Rui Liu;Xiangyu Mao;Chengxi Yang;Qiong Yan;Wenxiu Sun;Junkai Fang;Meimei Shang;Fei Gao;Sujoy Ghosh;Prasen Kumar Sharma;Arijit Sur;Wenjin Yang

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Year: 2019 | Conference Paper |

Cited by: Papers (30)

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This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallow depth-of-field by the Canon 70D DSLR camera. In this task, the participants had to restore bokeh e...Show More

AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results

Andrey Ignatov;Jagruti Patel;Radu Timofte;Bolun Zheng;Xin Ye;Li Huang;Xiang Tian;Saikat Dutta;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;A. N. Rajagopalan;Zhiwei Xiong;Jie Huang;Guanting Dong;Mingde Yao;Dong Liu;Ming Hong;Wenying Lin;Yanyun Qu;Jae-Seok Choi;Woonsung Park;Munchurl Kim;Rui Liu;Xiangyu Mao;Chengxi Yang;Qiong Yan;Wenxiu Sun;Junkai Fang;Meimei Shang;Fei Gao;Sujoy Ghosh;Prasen Kumar Sharma;Arijit Sur;Wenjin Yang

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Year: 2019 | Conference Paper |

A Real-Time Convolutional Neural Network for Super-Resolution on FPGA With Applications to 4K UHD 60 fps Video Services

Yongwoo Kim;Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2019 | Volume: 29, Issue: 8 | Journal Article |

Cited by: Papers (71)

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In this paper, we present a novel hardware-friendly super-resolution (SR) method based on a convolutional neural network (CNN) and its dedicated hardware (HW) on field programmable gate array (FPGA). Although CNN-based SR methods have shown very promising results for SR, their computational complexities are prohibitive for hardware implementation. To the best of our knowledge, we are the first to ...Show More

A Real-Time Convolutional Neural Network for Super-Resolution on FPGA With Applications to 4K UHD 60 fps Video Services

Yongwoo Kim;Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2019 | Volume: 29, Issue: 8 | Journal Article |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

Cited by: Papers (87)

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This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes

Hyeonjun Sim;Sehwan Ki;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

Cited by: Papers (18)

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Haze removal is one of the essential image enhancement processes that makes degraded images visually pleasing. Since haze in images often appears differently depending on the surroundings, haze removal requires the use of spatial information to effectively remove the haze according to the types of image region characteristics. However, in the conventional training, the small-sized training image p...Show More

High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes

Hyeonjun Sim;Sehwan Ki;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

Fully End-to-End Learning Based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing

Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

Cited by: Papers (21)

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A receptive field is defined as the region in an input image space that an output image pixel is looking at. Thus, the receptive field size influences the learning of deep convolution neural networks. Especially, in single image dehazing problems, larger receptive fields often show more effective dehazying by considering the brightness and color of the entire input hazy image without additional in...Show More

Fully End-to-End Learning Based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing

Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Soo Ye Kim;Soomin Seo;Saehun Kim;Munchurl Kim

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

Cited by: Papers (183)

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This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were lear...Show More

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Shuhang Gu;Jiqing Wu;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Shijia Hu;Yijie Bei;Zheng Hui;Xiao Jiang;Yanan Gu;Jie Liu;Yifan Wang;Federico Perazzi;Brian McWilliams;Alexander Sorkine-Hornung;Olga Sorkine-Hornung;Christopher Schroers;Jiahui Yu;Yuchen Fan;Jianchao Yang;Ning Xu;Zhaowen Wang;Xinchao Wang;Thomas S. Huang;Xintao Wang;Ke Yu;Tak-Wai Hui;Chao Dong;Liang Lin;Chen Change Loy;Dongwon Park;Kwanyoung Kim;Se Young Chun;Kai Zhang;Pengjv Liu;Wangmeng Zuo;Shi Guo;Jiye Liu;Jinchang Xu;Yijiao Liu;Fengye Xiong;Yuan Dong;Hongliang Bai;Alexandru Damian;Nikhil Ravi;Sachit Menon;Cynthia Rudin;Junghoon Seo;Taegyun Jeon;Jamyoung Koo;Seunghyun Jeon;Soo Ye Kim;Jae-Seok Choi;Sehwan Ki;Soomin Seo;Hyeonjun Sim;Saehun Kim;Munchurl Kim;Rong Chen;Kun Zeng;Jinkang Guo;Yanyun Qu;Cuihua Li;Namhyuk Ahn;Byungkon Kang;Kyung-Ah Sohn;Yuan Yuan;Jiawei Zhang;Jiahao Pang;Xiangyu Xu;Yan Zhao;Wei Deng;Sibt Ul Hussain;Muneeb Aadil;Rafia Rahim;Xiaowang Cai;Fang Huang;Yueshu Xu;Pablo Navarrete Michelini;Dan Zhu;Hanwen Liu;Jun-Hyuk Kim;Jong-Seok Lee;Yiwen Huang;Ming Qiu;Liting Jing;Jiehang Zeng;Ying Wang;Manoj Sharma;Rudrabha Mukhopadhyay;Avinash Upadhyay;Sriharsha Koundinya;Ankit Shukla;Santanu Chaudhury;Zhe Zhang;Yu Hen Hu;Lingzhi Fu

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2018 | Conference Paper |

2X Super-Resolution Hardware Using Edge-Orientation-Based Linear Mapping for Real-Time 4K UHD 60 fps Video Applications

Yongwoo Kim;Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2018 | Volume: 65, Issue: 9 | Journal Article |

Cited by: Papers (18)

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Based on our previous super-interpolation method, we propose a novel hardware-friendly super-resolution (SR) algorithm, called HSI method, and its dedicated hardware architecture for up-scaling full-high-definition (FHD) video streams to 4K ultra-high-definition (UHD) video streams in real-time. Our proposed HSI method involves training and up-scaling steps. In the training step, an edge-orientati...Show More

2X Super-Resolution Hardware Using Edge-Orientation-Based Linear Mapping for Real-Time 4K UHD 60 fps Video Applications

Yongwoo Kim;Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Circuits and Systems II: Express Briefs

Year: 2018 | Volume: 65, Issue: 9 | Journal Article |

A Deep Convolutional Neural Network with Selection Units for Super-Resolution

Jae-Seok Choi;Munchurl Kim

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

Cited by: Papers (88) | Patents (1)

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Rectified linear units (ReLU) are known to be effective in many deep learning methods. Inspired by linear-mapping technique used in other super-resolution (SR) methods, we reinterpret ReLU into point-wise multiplication of an identity mapping and a switch, and finally present a novel nonlinear unit, called a selection unit (SU). While conventional ReLU has no direct control through which data is p...Show More

A Deep Convolutional Neural Network with Selection Units for Super-Resolution

Jae-Seok Choi;Munchurl Kim

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;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;Chen Change Loy;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;Thomas S. Huang;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;Hojjat Seyed Mousavi;Tiep Huu Vu;Vishal Monga;Cristovao Cruz;Karen Egiazarian;Vladimir Katkovnik;Rakesh Mehta;Arnav Kumar Jain;Abhinav Agarwalla;Ch V. Sai Praveen;Ruofan Zhou;Hongdiao Wen;Che Zhu;Zhiqiang Xia;Zhengtao Wang;Qi Guo

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

Cited by: Papers (768) | Patents (1)

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This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 ...Show More

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;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;Chen Change Loy;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;Thomas S. Huang;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;Hojjat Seyed Mousavi;Tiep Huu Vu;Vishal Monga;Cristovao Cruz;Karen Egiazarian;Vladimir Katkovnik;Rakesh Mehta;Arnav Kumar Jain;Abhinav Agarwalla;Ch V. Sai Praveen;Ruofan Zhou;Hongdiao Wen;Che Zhu;Zhiqiang Xia;Zhengtao Wang;Qi Guo

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings

Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Image Processing

Year: 2017 | Volume: 26, Issue: 3 | Journal Article |

Cited by: Papers (42)

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Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our prev...Show More

Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings

Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Image Processing

Year: 2017 | Volume: 26, Issue: 3 | Journal Article |

Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex $2\times $ Upscaling

Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Image Processing

Year: 2016 | Volume: 25, Issue: 1 | Journal Article |

Cited by: Papers (34) | Patents (5)

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With the advent of ultrahigh-definition (UHD) video services, super-resolution (SR) techniques are often required to generate high-resolution (HR) images from low-resolution (LR) images, such as HD images. To generate such HR images and a video of UHD resolutions in limited computing devices with hardware and software, low complex but excellent SR methods are particularly required. In this paper, ...Show More

Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex $2\times $ Upscaling

Jae-Seok Choi;Munchurl Kim

IEEE Transactions on Image Processing

Year: 2016 | Volume: 25, Issue: 1 | Journal Article |

Single image super-resolution based on self-examples using context-dependent subpatches

Jae-Seok Choi;Sung-Ho Bae;Munchurl Kim

2015 IEEE International Conference on Image Processing (ICIP)

Year: 2015 | Conference Paper |

Cited by: Papers (6)

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Self-example-based super-resolution (SR) methods utilize internal dictionaries to reconstruct a high-resolution (HR) image from a single low-resolution (LR) input image. In general, a square-sized patch is used to find the LR-HR correspondences in the dictionaries. However, this may be a difficult issue because the LR input image and the dictionaries are of different scales. Inspired by this obser...Show More

Single image super-resolution based on self-examples using context-dependent subpatches

Jae-Seok Choi;Sung-Ho Bae;Munchurl Kim

2015 IEEE International Conference on Image Processing (ICIP)

Year: 2015 | Conference Paper |

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