Wenjie Feng - IEEE Xplore Author Profile

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Author details

Wenjie Feng

Also published under: Wen Jie Feng, Weijie Feng, Wenjie J. Feng, W. J. Feng, W. Feng

Publications

237

Citations

3,671

Publications by Year

20092025

Co-Authors:

Yang CaoYunfei CaoYue CaoYang-Lei CaoBaolin Cao

Show All Co-Authors (229)

Wenjie Feng

Also published under: Wen Jie Feng, Weijie Feng, Wenjie J. Feng, W. J. Feng, W. Feng

Affiliation

Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz

Guangdong-Hong Kong-Macao Joint Laboratory for Millimeter-Wave and Terahertz

School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

Publication Topics

Antenna Array,
Insertion Loss,
Radiation Pattern,
Resonance Frequency,
Sidelobe Level,
Aperture Efficiency,
Equivalent Circuit,
Feed Line,
Feeding Network,
High Gain,
Low Loss,
Metal Layer

Biography

Wenjie Feng (Senior Member, IEEE) was born in Shangqiu, Henan Province, China, in 1985. He received the B.S. degree from the First Aeronautic College of the Airforce, Xinyang, China, in 2008, the M.Sc., and Ph.D. degrees from the Nanjing University of Science and Technology (NUST), Nanjing, China, in 2010 and 2013, respectively. In 2017, he was a Research Fellow with the City University of Hong Kong, Hong Kong. From 2010 to 2011, he was an exchange student with the Institute of High-Frequency Engineering, Technische Universität München, Munich, Germany. He is currently a Professor with the Nanjing University of Science and Technology, Nanjing, China, and also a Visiting Professor with the South China University of Technology, Guangzhou, Chi...

Publications

237

Citations

3,671

Publications by Year

20092025

Co-Authors:

Yang Cao
Yunfei Cao
Yue Cao
Yang-Lei Cao
Baolin Cao

Show All Co-Authors (229)

Author's Published Works

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Showing 1-25 of 30 results

Conferences (15)

Journals (15)

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Kun Zeng(30)
Liang Lin(4)
Jinsheng Fang(4)
Guobao Xiao(4)
Zuoyong Li(4)
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China(6)
School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China(3)
College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China(2)
Sun Yat-Sen University, Guangzhou, China(2)
College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China(2)
IEEE Access(5)
IEEE Transactions on Instrumentation and Measurement(3)
IEEE Transactions on Multimedia(3)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
IEEE(30)
Beijing, China(2)
New Orleans, LA, USA(2)
Salt Lake City, UT, USA(2)
Xiamen, China(2)
Abu Dhabi, United Arab Emirates(1)
Convolutional Neural Network(13)
Convolutional Layers(11)
Deep Learning(11)
High-resolution Images(11)
Low-resolution Images(11)

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Results

WaveSpect: A Hybrid Approach to Synthetic Audio Detection via Waveform and Spectrogram Analysis

Dong Chen;Fan Huang;Zhengxuan Song;Wei Zhu;Yin Yang;Kun Zeng

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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With the rapid advancement of synthetic speech technology, the challenges posed by audio deepfakes have become increasingly severe. Despite notable progress in synthetic speech detection, existing algorithms exhibit limited generalization to unknown attacks. To address these challenges, we propose WaveSpect, which combines waveform and spectrogram features to capture subtle artifacts typically ove...Show More

WaveSpect: A Hybrid Approach to Synthetic Audio Detection via Waveform and Spectrogram Analysis

Dong Chen;Fan Huang;Zhengxuan Song;Wei Zhu;Yin Yang;Kun Zeng

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

Shiying Lin;Rong Hu;Zuoyong Li;Qinghua Lin;Kun Zeng;Xiang Wu

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

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In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixe...Show More

KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

Shiying Lin;Rong Hu;Zuoyong Li;Qinghua Lin;Kun Zeng;Xiang Wu

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

A Generic Multicorrespondence Matching Framework for Reference-Based Image Super-Resolution

Hong Zheng;Huabin Heng;Zhiqiang Yan;Kun Zeng;Jinsheng Fang;Baohua Qiang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

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Image super-resolution (SR) plays an important role in vision-based measurements, and reference-based image super-resolution (RefSR) is one such SR technique that enhances the resolution of a low-resolution (LR) image using an external high-resolution (HR) reference image. However, most existing RefSR methods search for the best-matching correspondence in the reference image, typically a single pa...Show More

A Generic Multicorrespondence Matching Framework for Reference-Based Image Super-Resolution

Hong Zheng;Huabin Heng;Zhiqiang Yan;Kun Zeng;Jinsheng Fang;Baohua Qiang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

ITSMatch: Improved Safe Semi-Supervised Text Classification Under Class Distribution Mismatch

Kun Zeng;Jinlin Li;Yibin Xu;Xinjing Zhang;Ge Lin;Nanli Zeng;Yingying Qu

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 2 | Journal Article |

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Deep semi-supervised learning (SSL) brings deep learning from lab with expensive label data costs to real-world commercial application. Today, deep SSL is being universally applied in various artificial intelligence commercial technologies. However, there may be a distribution mismatch between labeled and unlabeled datasets in practical application, which is a key issue that degrades deep SSL perf...Show More

ITSMatch: Improved Safe Semi-Supervised Text Classification Under Class Distribution Mismatch

Kun Zeng;Jinlin Li;Yibin Xu;Xinjing Zhang;Ge Lin;Nanli Zeng;Yingying Qu

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 2 | Journal Article |

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Tingting Zhao;Zuoyong Li;Li Quan;Kun Zeng;Shenghua Teng

2023 13th International Conference on Information Technology in Medicine and Education (ITME)

Year: 2023 | Conference Paper |

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The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve ...Show More

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Tingting Zhao;Zuoyong Li;Li Quan;Kun Zeng;Shenghua Teng

2023 13th International Conference on Information Technology in Medicine and Education (ITME)

Year: 2023 | Conference Paper |

Densely Connected Transformer With Linear Self-Attention for Lightweight Image Super-Resolution

Kun Zeng;Hanjiang Lin;Zhiqiang Yan;Jinsheng Fang

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

Cited by: Papers (4)

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Image super-resolution (SR) is the process of restoring high-resolution (HR) images from low-resolution (LR) ones. Recent Transformer-based SR methods have achieved impressive results by utilizing the self-attention (SA) mechanism, which allows modeling long-range dependencies among input features in spatial dimensions. However, the computational complexity of SA increases quadratically with respe...Show More

Densely Connected Transformer With Linear Self-Attention for Lightweight Image Super-Resolution

Kun Zeng;Hanjiang Lin;Zhiqiang Yan;Jinsheng Fang

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

A Network Combining CNN and Transformer for Blind Image Super-Resolution

Shuhao Zhang;Zuoyong Li;Shenghua Teng;Kun Zeng

2022 12th International Conference on Information Technology in Medicine and Education (ITME)

Year: 2022 | Conference Paper |

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Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining ...Show More

A Network Combining CNN and Transformer for Blind Image Super-Resolution

Shuhao Zhang;Zuoyong Li;Shenghua Teng;Kun Zeng

2022 12th International Conference on Information Technology in Medicine and Education (ITME)

Year: 2022 | Conference Paper |

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Cited by: Papers (44)

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This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficien...Show More

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li;Kai Zhang;Radu Timofte;Luc Van Gool;Fangyuan Kong;Mingxi Li;Songwei Liu;Zongcai Du;Ding Liu;Chenhui Zhou;Jingyi Chen;Qingrui Han;Zheyuan Li;Yingqi Liu;Xiangyu Chen;Haoming Cai;Yu Qiao;Chao Dong;Long Sun;Jinshan Pan;Yi Zhu;Zhikai Zong;Xiaoxiao Liu;Zheng Hui;Tao Yang;Peiran Ren;Xuansong Xie;Xian-Sheng Hua;Yanbo Wang;Xiaozhong Ji;Chuming Lin;Donghao Luo;Ying Tai;Chengjie Wang;Zhizhong Zhang;Yuan Xie;Shen Cheng;Ziwei Luo;Lei Yu;Zhihong Wen;Qi Wul;Youwei Li;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Yuanfei Huang;Meiguang Jin;Hua Huang;Jing Liu;Xinjian Zhang;Yan Wang;Lingshun Long;Gen Li;Yuanfan Zhang;Zuowei Cao;Lei Sun;Panaetov Alexander;Yucong Wang;Minjie Cai;Li Wang;Lu Tian;Zheyuan Wang;Hongbing Ma;Jie Liu;Chao Chen;Yidong Cai;Jie Tang;Gangshan Wu;Weiran Wang;Shirui Huang;Honglei Lu;Huan Liu;Keyan Wang;Jun Chen;Shi Chen;Yuchun Miao;Zimo Huang;Lefei Zhang;Mustafa Ayazoğlu;Wei Xiong;Chengyi Xiong;Fei Wang;Hao Li;Ruimian Wen;Zhijing Yang;Wenbin Zou;Weixin Zheng;Tian Ye;Yuncheng Zhang;Xiangzhen Kong;Aditya Arora;Syed Waqas Zamir;Salman Khan;Munawar Hayat;Fahad Shahbaz Khan;Dandan Gao;Dengwen Zhou;Qian Ning;Jingzhu Tang;Han Huang;Yufei Wang;Zhangheng Peng;Haobo Li;Wenxue Guan;Shenghua Gong;Xin Li;Jun Liu;Wanjun Wang;Dengwen Zhou;Kun Zeng;Hanjiang Lin;Xinyu Chen;Jinsheng Fang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

Jinsheng Fang;Hanjiang Lin;Xinyu Chen;Kun Zeng

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Cited by: Papers (86)

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Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network ...Show More

A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

Jinsheng Fang;Hanjiang Lin;Xinyu Chen;Kun Zeng

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Structured Attention Network for Referring Image Segmentation

Liang Lin;Pengxiang Yan;Xiaoqian Xu;Sibei Yang;Kun Zeng;Guanbin Li

IEEE Transactions on Multimedia

Year: 2022 | Volume: 24 | Journal Article |

Cited by: Papers (27)

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Referring image segmentation aims at segmenting out the object or stuff referred to by a natural language expression. The challenge of this task lies in the requirement of understanding both vision and language. The linguistic structure of a referring expression can provide an intuitive and explainable layout for reasoning over visual and linguistic concepts. In this paper, we propose a structured...Show More

Structured Attention Network for Referring Image Segmentation

Liang Lin;Pengxiang Yan;Xiaoqian Xu;Sibei Yang;Kun Zeng;Guanbin Li

IEEE Transactions on Multimedia

Year: 2022 | Volume: 24 | Journal Article |

Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

Guobao Xiao;Huan Luo;Kun Zeng;Leyi Wei;Jiayi Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (12)

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Feature matching is a fundamental problem in feature-based remote sensing image registration. Due to the ground relief variations and imaging viewpoint changes, remote sensing images often involve local distortions, leading to difficulties in high-accuracy image registration. To address this issue, in this article, we propose a robust feature matching method called First Neighbor Relation Guided (...Show More

Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting

Guobao Xiao;Huan Luo;Kun Zeng;Leyi Wei;Jiayi Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Hypersonic Vehicle Trajectory Classification Using Improved CNN-LSTM Model

Kun Zeng;Xuebin Zhuang;Yangfan Xie;Zepu Xi

2021 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2021 | Conference Paper |

Cited by: Papers (4)

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Hypersonic vehicle has become a hotspot of aircraft research because of its high speed and complex maneuvering mode. The classification of the trajectory of hypersonic vehicle is of great significance to the trajectory prediction and interception of hypersonic vehicles. This paper proposes a neural network structure combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (L...Show More

Hypersonic Vehicle Trajectory Classification Using Improved CNN-LSTM Model

Kun Zeng;Xuebin Zhuang;Yangfan Xie;Zepu Xi

2021 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2021 | Conference Paper |

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Andrey Ignatov;Radu Timofte;Maurizio Denna;Abdel Younes;Andrew Lek;Mustafa Ayazoglu;Jie Liu;Zongcai Du;Jiaming Guo;Xueyi Zhou;Hao Jia;Youliang Yan;Zexin Zhang;Yixin Chen;Yunbo Peng;Yue Lin;Xindong Zhang;Hui Zeng;Kun Zeng;Peirong Li;Zhihuang Liu;Shiqi Xue;Shengpeng Wang

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

Year: 2021 | Conference Paper |

Cited by: Papers (45)

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Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first M...Show More

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Andrey Ignatov;Radu Timofte;Maurizio Denna;Abdel Younes;Andrew Lek;Mustafa Ayazoglu;Jie Liu;Zongcai Du;Jiaming Guo;Xueyi Zhou;Hao Jia;Youliang Yan;Zexin Zhang;Yixin Chen;Yunbo Peng;Yue Lin;Xindong Zhang;Hui Zeng;Kun Zeng;Peirong Li;Zhihuang Liu;Shiqi Xue;Shengpeng Wang

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

Year: 2021 | Conference Paper |

Occlusion-Guided Vertical Retargeting For Stereoscopic Images Based On Pixel Fusion

Jiangchuan Hu;Kun Zeng;Kanoksak Wattanachote;Yongyi Gong

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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To implement vertical retargeting for stereoscopic images, this paper proposes an occlusion-guided stereoscopic image retargeting method via the pixel fusion technique. Traditional seam searching-based methods cannot construct valid horizontal seam pairs due to the existence of occluded regions in stereoscopic images and thus fail to implement vertical retargeting. To solve this issue, we propose ...Show More

Occlusion-Guided Vertical Retargeting For Stereoscopic Images Based On Pixel Fusion

Jiangchuan Hu;Kun Zeng;Kanoksak Wattanachote;Yongyi Gong

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

A Convolutional Attention Residual Network for Stereo Matching

Guangyi Huang;Yongyi Gong;Qingzhen Xu;Kanoksak Wattanachote;Kun Zeng;Xiaonan Luo

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (22)

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Deep learning based on convolutional neural network (CNN) has been successfully applied to stereo matching, which has achieved greater improvement in speed and accuracy compared with traditional methods. However, existing CNN-based stereo matching frameworks frequently encounter two problems. First, the existing stereo matching network has a large number of parameters, which results in too long ma...Show More

A Convolutional Attention Residual Network for Stereo Matching

Guangyi Huang;Yongyi Gong;Qingzhen Xu;Kanoksak Wattanachote;Kun Zeng;Xiaonan Luo

Year: 2020 | Volume: 8 | Journal Article |

Sketch-Based Shape Retrieval via Best View Selection and a Cross-Domain Similarity Measure

Yongzhe Xu;Jiangchuan Hu;Kanoksak Wattanachote;Kun Zeng;YongYi Gong

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 11 | Journal Article |

Cited by: Papers (21)

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Retrieving 3D shapes from 2D human sketches has received increasing attention in computer vision and computer graphics. Most previous methods projected 3D shapes from numerous viewpoints and then extracted features of 3D shapes from these projections and calculated the similarity with sketches. However, due to the unknown pose of 3D shapes, viewpoints were usually sampled uniformly from a sphere c...Show More

Sketch-Based Shape Retrieval via Best View Selection and a Cross-Domain Similarity Measure

Yongzhe Xu;Jiangchuan Hu;Kanoksak Wattanachote;Kun Zeng;YongYi Gong

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 11 | Journal Article |

Identification and Prediction of Key Nucleotide Sites Using Machine Learning in Bioinformatics: A Brief Overview

Jianhua Cai;Leyi Wei;Kun Zeng;Guobao Xiao

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2019 | Conference Paper |

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Nowadays, the burgeon of machine learning has promoted its wide application in various fields. In Bioinformatics, machine learning computational method has become an indispensable part. Its efficiency and simplicity break the expensive and inefficient barriers of previous experimental methods. This article briefly reviews the application and development of machine learning in identifying and predi...Show More

Identification and Prediction of Key Nucleotide Sites Using Machine Learning in Bioinformatics: A Brief Overview

Jianhua Cai;Leyi Wei;Kun Zeng;Guobao Xiao

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

Year: 2019 | Conference Paper |

White Blood Cell Segmentation via Sparsity and Geometry Constraints

Zhen Zhong;Tao Wang;Kun Zeng;Xiaogen Zhou;Zuoyong Li

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (15)

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Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentatio...Show More

White Blood Cell Segmentation via Sparsity and Geometry Constraints

Zhen Zhong;Tao Wang;Kun Zeng;Xiaogen Zhou;Zuoyong Li

Year: 2019 | Volume: 7 | Journal Article |

A Very Deep Densely Connected Network for Compressed Sensing MRI

Kun Zeng;Yu Yang;Guobao Xiao;Zhong Chen

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (22)

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Convolutional neural network (CNN) has achieved great success in the compressed sensing-based magnetic resonance imaging (CS-MRI). Latest deep networks for CS-MRI usually consist of a stack of sub-networks, each of which refines the former image prediction to a more accurate one. However, as the sub-network number increases, the information in prior sub-networks has a little influence on subsequen...Show More

A Very Deep Densely Connected Network for Compressed Sensing MRI

Kun Zeng;Yu Yang;Guobao Xiao;Zhong Chen

Year: 2019 | Volume: 7 | Journal Article |

Discriminant Affinity Matrix for Deterministic Motion Trajectory Segmentation

Guobao Xiao;Kun Zeng;Leyi Wei;Tao Wang;Taotao Lai

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (1)

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We present a novel algorithm (DAM) for deterministic motion trajectory segmentation by using epipolar geometry and adaptive kernel-scale voting. DAM is based on geometric models and exploits the information derived from superpixels to deterministically construct a set of initial correlation matrices. Then DAM introduces a novel adaptive kernel-scale voting scheme to measure each initial correlatio...Show More

Discriminant Affinity Matrix for Deterministic Motion Trajectory Segmentation

Guobao Xiao;Kun Zeng;Leyi Wei;Tao Wang;Taotao Lai

Year: 2019 | Volume: 7 | Journal Article |

Sketch-Based Shape Retrieval via Multi-view Attention and Generalized Similarity

Yongzhe Xu;Jiangchuan Hu;Kun Zeng;Yongyi Gong

2018 7th International Conference on Digital Home (ICDH)

Year: 2018 | Conference Paper |

Cited by: Papers (5)

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Sketch-based shape retrieval has received increasing attention in computer vision and computer graphics. It suffers from the challenge gap between 2D sketches and 3D shapes. In this paper, we propose a generalized similarity matching framework based on a multi-view attention network (MVAN), which can retrieve 3D shape that is most similar to the query sketch. In proposed approach, firstly we compu...Show More

Sketch-Based Shape Retrieval via Multi-view Attention and Generalized Similarity

Yongzhe Xu;Jiangchuan Hu;Kun Zeng;Yongyi Gong

2018 7th International Conference on Digital Home (ICDH)

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 |

Persistent Memory Residual Network for Single Image Super Resolution

Rong Chen;Yanyun Qu;Kun Zeng;Jinkang Guo;Cuihua Li;Yuan Xie

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

Year: 2018 | Conference Paper |

Cited by: Papers (13)

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Progresses has been witnessed in single image superresolution in which the low-resolution images are simulated by bicubic downsampling. However, for the complex image degradation in the wild such as downsampling, blurring, noises, and geometric deformation, the existing superresolution methods do not work well. Inspired by a persistent memory network which has been proven to be effective in image ...Show More

Persistent Memory Residual Network for Single Image Super Resolution

Rong Chen;Yanyun Qu;Kun Zeng;Jinkang Guo;Cuihua Li;Yuan Xie

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

Year: 2018 | Conference Paper |

Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations

Kun Zeng;Hong Zheng;Yanyun Qu;Xiaobo Qu;Lijun Bao;Zhong Chen

2018 24th International Conference on Pattern Recognition (ICPR)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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Conventional sparse coding based super-resolution (SR) methods obtained promising performance by learning overcomplete dictionaries for low-resolution (LR) and high-resolution (HR) feature spaces, and assuming that the sparse representation of a HR feature vector was identical or linear to the sparse representation of the corresponding LR one. However, in fact, the relationship between LR and HR s...Show More

Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations

Kun Zeng;Hong Zheng;Yanyun Qu;Xiaobo Qu;Lijun Bao;Zhong Chen

2018 24th International Conference on Pattern Recognition (ICPR)

Year: 2018 | Conference Paper |

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Hong Zheng;Kun Zeng;Di Guo;Jiaxi Ying;Yu Yang;Xi Peng;Feng Huang;Zhong Chen;Xiaobo Qu

Year: 2018 | Volume: 6 | Journal Article |

Cited by: Papers (39)

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In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of g...Show More

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Hong Zheng;Kun Zeng;Di Guo;Jiaxi Ying;Yu Yang;Xi Peng;Feng Huang;Zhong Chen;Xiaobo Qu

Year: 2018 | Volume: 6 | Journal Article |

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