Loading [MathJax]/extensions/MathMenu.js

Li-Wen Wang - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Li-Wen Wang

Also published under: Liwen Wang

Publications

17

Citations

406

Publications by Year

20182024

Co-Authors:

Rajagopalan A. N.Kazutoshi AkitaSun AnshunAditya AroraFurui Bai

Show All Co-Authors (197)

Li-Wen Wang

Also published under: Liwen Wang

Affiliation

Saint Francis University, Hong Kong

Hong Kong Polytechnic University

Publication Topics

Convolutional Neural Network,
Convolutional Layers,
Digital Micromirror Device,
Feature Maps,
Intensity Measurements,
Phase Retrieval,
Absolute Coordinates,
Artistic Style Transfer,
Attention Mechanism,
Bounding Box,
CNN-based Approaches,
Consistency Loss

Biography

Li-Wen Wang (Graduate Student Member, IEEE) received the Bachelor of Engineering degree from Shandong University in 2016 and the M.Sc. degree (Hons.) from The Hong Kong Polytechnic University, where he is currently pursuing the Ph.D. degree under the supervision of Prof. Wan-Chi Siu and Dr. Daniel P. K. Lun. His research interests include deep learning techniques, image and video processing, object detection and tracking, and autonomous driving.(Based on document published on 15 July 2020).

Publications

17

Citations

406

Publications by Year

20182024

Co-Authors:

Rajagopalan A. N.
Kazutoshi Akita
Sun Anshun
Aditya Arora
Furui Bai

Show All Co-Authors (197)

Author's Published Works

Search History

Showing 1-17 of 17 results

Conferences (15)

Journals (2)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
Li-Wen Wang(16)
Wan-Chi Siu(13)
Zhi-Song Liu(12)
Chu-Tak Li(7)
Daniel Pak-Kong Lun(4)
The Hong Kong Polytechnic University(5)
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China(2)
Caritas Institute of Higher Education(2)
Shenzhen Research Institute of Northwestern Polytechnical University, Guangdong, China(1)
VISTA, LIX, Ecole Polytechnique, IP Paris(1)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)(2)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)(1)
2020 25th International Conference on Pattern Recognition (ICPR)(1)
IEEE(17)
Video(1)
Long Beach, CA, USA(2)
Nashville, TN, USA(2)
Seoul, Korea (South)(2)
Hong Kong, Hong Kong(1)
Las Vegas, NV, USA(1)
Feature Maps(11)
Convolutional Layers(10)
Convolutional Neural Network(10)
Generative Adversarial Networks(8)
Low-resolution Images(7)

Select All on Page

Sort By

Results

Video Assisted Face Recognition in Smart Classroom

Li-Wen Wang;Wan-Chi Siu;Yi-Hao Cheng;H. Anthony Chan

2024 IEEE International Symposium on Circuits and Systems (ISCAS)

Year: 2024 | Conference Paper |

HTML

Student recognition in smart classrooms is challenging due to low resolution, occlusions, and various face orientations. We propose a video-based approach for robust face recognition. It firstly collects a continuous sequence of face images through inter-frame analysis. Then, it aggregates face features, via statistical elimination of outliers. We introduce a new consistency loss to address varyin...Show More

Video Assisted Face Recognition in Smart Classroom

Li-Wen Wang;Wan-Chi Siu;Yi-Hao Cheng;H. Anthony Chan

2024 IEEE International Symposium on Circuits and Systems (ISCAS)

Year: 2024 | Conference Paper |

Multiple-Level Green Noise Mask Design for Practical Fourier Phase Retrieval

Qiuliang Ye;Bingo Wing-Kuen Ling;Li-Wen Wang;Daniel Pak-Kong Lun

IEEE Transactions on Signal Processing

Year: 2024 | Volume: 72 | Journal Article |

HTML

Phase retrieval, a long-established challenge for recovering a complex-valued signal from its Fourier intensity measurements, has attracted significant interest because of its far-flung applications in optical imaging. To enhance accuracy, researchers introduce extra constraints to the measuring procedure by including a random aperture mask in the optical path that randomly modulates the light pro...Show More

Multiple-Level Green Noise Mask Design for Practical Fourier Phase Retrieval

Qiuliang Ye;Bingo Wing-Kuen Ling;Li-Wen Wang;Daniel Pak-Kong Lun

IEEE Transactions on Signal Processing

Year: 2024 | Volume: 72 | Journal Article |

Name your style: text-guided artistic style transfer

Zhi-Song Liu;Li-Wen Wang;Wan-Chi Siu;Vicky Kalogeiton

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

HTML

Image style transfer has attracted widespread attention in the past years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way to describe the style. Text can describe implicit abstract styles, like styles of specific artists or art movements. In this work, we propose a text-driven...Show More

Name your style: text-guided artistic style transfer

Zhi-Song Liu;Li-Wen Wang;Wan-Chi Siu;Vicky Kalogeiton

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Physics-driven Deep Neural Network for Fourier Phase Retrieval

Qiuliang Ye;Liwen Wang;Daniel P.K. Lun

TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)

Year: 2022 | Conference Paper |

HTML

Fourier Phase retrieval (PR), aiming at recovering a complex-valued signal from its Fourier intensity measurements, has attracted widespread attention due to its importance in many optical imaging applications. Recently, deep learning-based approaches were developed that achieved some success. These approaches require only a single Fourier intensity measurement without the need to impose any addit...Show More

Physics-driven Deep Neural Network for Fourier Phase Retrieval

Qiuliang Ye;Liwen Wang;Daniel P.K. Lun

TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)

Year: 2022 | Conference Paper |

Highly Reliable Vehicle Detection through CNN with Attention Mechanism

Li-Wen Wang;Wan-Chi Siu;Xue-Fei Yang;Zhi-Song Liu;Daniel Pak-Kong Lun

2022 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

HTML

This paper presents a novel approach to provide reliable vehicle detection by using CNN-based approach and lane information. We firstly propose an adaptive RoI strategy that utilizes road lane information to give focus on frontal area for vehicle detection. Then we introduce a novel attention mechanism that automatically learns an attention map to refine the features for detection. Experimental re...Show More

Highly Reliable Vehicle Detection through CNN with Attention Mechanism

Li-Wen Wang;Wan-Chi Siu;Xue-Fei Yang;Zhi-Song Liu;Daniel Pak-Kong Lun

2022 IEEE International Conference on Consumer Electronics (ICCE)

Year: 2022 | Conference Paper |

Variational AutoEncoder for Reference based Image Super-Resolution

Zhi-Song Liu;Wan-Chi Siu;Li-Wen Wang

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (15)

HTML

In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e.g., 8×. We propose a reference based image super-resolution, for which any arbitrary image can act as a reference for super-resolution. Even us...Show More

Variational AutoEncoder for Reference based Image Super-Resolution

Zhi-Song Liu;Wan-Chi Siu;Li-Wen Wang

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

NTIRE 2021 Learning the Super-Resolution Space Challenge

Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (24)

HTML

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a ...Show More

NTIRE 2021 Learning the Super-Resolution Space Challenge

Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Video Lightening with Dedicated CNN Architecture

Li-Wen Wang;Wan-Chi Siu;Zhi-Song Liu;Chu-Tak Li;Daniel Pak-Kong Lun

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

HTML

Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is us...Show More

Video Lightening with Dedicated CNN Architecture

Li-Wen Wang;Wan-Chi Siu;Zhi-Song Liu;Chu-Tak Li;Daniel Pak-Kong Lun

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Zixun Huang;Li-Wen Wang;Frank H. F. Leung;Sunetra Banerjee;De Yang;Timothy Lee;Juan Lyu;Sai Ho Ling;Yong-Ping Zheng

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2020 | Conference Paper |

Cited by: Papers (12)

HTML

3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to its low-costing, radiation-free and real-time characteristics. The key to accessing scoliosis by ultrasound imaging is to accurately segment the bone area and measure the scoliosis degree based on the symmetry of the bone features. The ultrasound images tend to contain many speckles and regular occlusion noise which is dif...Show More

Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Zixun Huang;Li-Wen Wang;Frank H. F. Leung;Sunetra Banerjee;De Yang;Timothy Lee;Juan Lyu;Sai Ho Ling;Yong-Ping Zheng

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2020 | Conference Paper |

Deep Lightening Network for Low-Light Image Enhancement

Li-Wen Wang;Zhi-Song Liu;Wan-Chi Siu;Daniel Pak-Kong Lun

2020 IEEE International Symposium on Circuits and Systems (ISCAS)

Year: 2020 | Conference Paper |

Cited by: Papers (6)

HTML

We propose a Deep Lightening Network (DLN) for low-light image enhancement. Inspire by the domain transfer study, we propose a novel cycle learning structure to learn the mapping relationship between low- and normal-light images. Each DLN consists of several Lightening Back-Projection (LBP) blocks that learn the residual between low- and normal-light images. To efficiently estimate the local and g...Show More

Deep Lightening Network for Low-Light Image Enhancement

Li-Wen Wang;Zhi-Song Liu;Wan-Chi Siu;Daniel Pak-Kong Lun

2020 IEEE International Symposium on Circuits and Systems (ISCAS)

Year: 2020 | Conference Paper |

Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder

Zhi-Song Liu;Wan-Chi Siu;Li-Wen Wang;Chu-Tak Li;Marie-Paule Cani;Yui-Lam Chan

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

Year: 2020 | Conference Paper |

Cited by: Papers (24)

HTML

Benefited from the deep learning, image Super-Resolution has been one of the most developing research fields in computer vision. Depending upon whether using a discriminator or not, a deep convolutional neural network can provide an image with high fidelity or better perceptual quality. Due to the lack of ground truth images in real life, people prefer a photo-realistic image with low fidelity to ...Show More

Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder

Zhi-Song Liu;Wan-Chi Siu;Li-Wen Wang;Chu-Tak Li;Marie-Paule Cani;Yui-Lam Chan

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

Year: 2020 | Conference Paper |

Lightening Network for Low-Light Image Enhancement

Li-Wen Wang;Zhi-Song Liu;Wan-Chi Siu;Daniel P. K. Lun

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Cited by: Papers (165)

HTML

Low-light image enhancement is a challenging task that has attracted considerable attention. Pictures taken in low-light conditions often have bad visual quality. To address the problem, we regard the low-light enhancement as a residual learning problem that is to estimate the residual between low- and normal-light images. In this paper, we propose a novel Deep Lightening Network (DLN) that benefi...Show More

Lightening Network for Low-Light Image Enhancement

Li-Wen Wang;Zhi-Song Liu;Wan-Chi Siu;Daniel P. K. Lun

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Hierarchical Back Projection Network for Image Super-Resolution

Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

Cited by: Papers (32)

HTML

Deep learning based single image super-resolution methods use a large number of training datasets and have recently achieved great quality progress both quantitatively and qualitatively. Most deep networks focus on nonlinear mapping from low-resolution inputs to high-resolution outputs via residual learning without exploring the feature abstraction and analysis. We propose a Hierarchical Back Proj...Show More

Hierarchical Back Projection Network for Image Super-Resolution

Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Jianrui Cai;Shuhang Gu;Radu Timofte;Lei Zhang;Xiao Liu;Yukang Ding;Dongliang He;Chao Li;Yi Fu;Shilei Wen;Ruicheng Feng;Jinjin Gu;Yu Qiao;Chao Dong;Dongwon Park;Se Young Chun;Sanghoon Yoon;Junhyung Kwak;Donghee Son;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Zhengping Wei;Lei Liu;Hong Cai;Darui Li;Fujie Gao;Zheng Hui;Xiumei Wang;Xinbo Gao;Guoan Cheng;Ai Matsune;Qiuyu Li;Leilei Zhu;Huaijuan Zang;Shu Zhan;Yajun Qiu;Ruxin wang;Jiawei Li;Yongcheng Jing;Mingli Song;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Wenyi Tang;Jing Liu;Youngjung Kim;Changyeop Shin;Minbeom Kim;Sungho Kim;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Xuan Xu;Xin Li;Furui Bai;Xiaopeng Sun;Lin Zha;Yuanfei Huang;Wen Lu;Yanpeng Cao;Du Chen;Zewei He;Sun Anshun;Siliang Tang;Hongfei Fan;Xiang Li;Guo Li;Wenjie Zhang;Yumei Zhang;Qingwen He;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Jun Yu;Guochen Xie;Mengyan Li;Rong Chen;Xiaotong Luo;Chen Hong;Yanyun Qu;Cuihua Li;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Can Zhao;Bowen Li;Chung-Chi Tsai;Shang-Chih Chuang;Joon-Hee Choi;Joonsoo Kim;Xiaoyun Jiang;Ze Pan;Qunbo Lv;Zheng Tan;Peidong He

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

Cited by: Papers (43)

HTML

This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter...Show More

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Jianrui Cai;Shuhang Gu;Radu Timofte;Lei Zhang;Xiao Liu;Yukang Ding;Dongliang He;Chao Li;Yi Fu;Shilei Wen;Ruicheng Feng;Jinjin Gu;Yu Qiao;Chao Dong;Dongwon Park;Se Young Chun;Sanghoon Yoon;Junhyung Kwak;Donghee Son;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Zhengping Wei;Lei Liu;Hong Cai;Darui Li;Fujie Gao;Zheng Hui;Xiumei Wang;Xinbo Gao;Guoan Cheng;Ai Matsune;Qiuyu Li;Leilei Zhu;Huaijuan Zang;Shu Zhan;Yajun Qiu;Ruxin wang;Jiawei Li;Yongcheng Jing;Mingli Song;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Wenyi Tang;Jing Liu;Youngjung Kim;Changyeop Shin;Minbeom Kim;Sungho Kim;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Xuan Xu;Xin Li;Furui Bai;Xiaopeng Sun;Lin Zha;Yuanfei Huang;Wen Lu;Yanpeng Cao;Du Chen;Zewei He;Sun Anshun;Siliang Tang;Hongfei Fan;Xiang Li;Guo Li;Wenjie Zhang;Yumei Zhang;Qingwen He;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Jun Yu;Guochen Xie;Mengyan Li;Rong Chen;Xiaotong Luo;Chen Hong;Yanyun Qu;Cuihua Li;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Can Zhao;Bowen Li;Chung-Chi Tsai;Shang-Chih Chuang;Joon-Hee Choi;Joonsoo Kim;Xiaoyun Jiang;Ze Pan;Qunbo Lv;Zheng Tan;Peidong He

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

Image Super-Resolution via Attention Based Back Projection Networks

Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu;Yui-Lam Chan

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

Year: 2019 | Conference Paper |

Cited by: Papers (50)

HTML

Deep learning based image Super-Resolution (SR) has shown rapid development due to its ability of big data digestion. Generally, deeper and wider networks can extract richer feature maps and generate SR images with remarkable quality. However, the more complex network we have, the more time consumption is required for practical applications. It is important to have a simplified network for efficie...Show More

Image Super-Resolution via Attention Based Back Projection Networks

Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu;Yui-Lam Chan

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

Year: 2019 | Conference Paper |

AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

Shuhang Gu;Martin Danelljan;Radu Timofte;Muhammad Haris;Kazutoshi Akita;Greg Shakhnarovic;Norimichi Ukita;Pablo Navarrete Michelini;Wenbin Chen;Hanwen Liu;Dan Zhu;Tangxin Xie;Xin Yang;Chen Zhu;Jia Yu;Wenyu Sun;Xin Tao;Zijun Deng;Liying Lu;Wenbo Li;Taian Guo;Xiaoyong Shen;Xuemiao Xu;Yu-Wing Tai;Jiaya Jia;Peng Yi;Zhongyuan Wang;Kui Jiang;Junjun Jiang;Jiayi Ma;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu;Yui-Lam Chan;Ruofan Zhou;Majed EI Helou;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;Rajagopalan A.N

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

Year: 2019 | Conference Paper |

Cited by: Papers (21)

HTML

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, ×16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1...Show More

AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

Shuhang Gu;Martin Danelljan;Radu Timofte;Muhammad Haris;Kazutoshi Akita;Greg Shakhnarovic;Norimichi Ukita;Pablo Navarrete Michelini;Wenbin Chen;Hanwen Liu;Dan Zhu;Tangxin Xie;Xin Yang;Chen Zhu;Jia Yu;Wenyu Sun;Xin Tao;Zijun Deng;Liying Lu;Wenbo Li;Taian Guo;Xiaoyong Shen;Xuemiao Xu;Yu-Wing Tai;Jiaya Jia;Peng Yi;Zhongyuan Wang;Kui Jiang;Junjun Jiang;Jiayi Ma;Zhi-Song Liu;Li-Wen Wang;Chu-Tak Li;Wan-Chi Siu;Yui-Lam Chan;Ruofan Zhou;Majed EI Helou;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;Rajagopalan A.N

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

Year: 2019 | Conference Paper |

Learning Approach with Random Forests on Vehicle Detection

Li-Wen Wang;Xue-Fei Yang;Wan-Chi Siu

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)

Year: 2018 | Conference Paper |

Cited by: Papers (6)

HTML

A Close-up Monitoring System (CMS) has been designed in our research laboratory, which aims at avoiding any potential collision risk by detecting the frontal train's distance from the captured video. Histogram of orientated gradient (HOG) has been used as a feature descriptor, because it gives robust performance in various illumination conditions. Random forest algorithm is a conventional machine ...Show More

Learning Approach with Random Forests on Vehicle Detection

Li-Wen Wang;Xue-Fei Yang;Wan-Chi Siu

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)

Year: 2018 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.