Dragana Perkovic - 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

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Dragana Perkovic

Also published under: D. Perkovic

Publications

18

Citations

176

Publications by Year

19992013

Co-Authors:

M. BehnJames R. CarswellP. ChangTao ChuR. Contreras

Show All Co-Authors (28)

Dragana Perkovic

Also published under: D. Perkovic

Affiliation

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, CA, USA

Publication Topics

Calibration Algorithm,
Calibration Signal,
Digital Beamforming,
Digital Calibration,
Impulse Response,
New Instrument,
Phase Shift,
Solid Biomass,
Synthetic Aperture Radar,
Analog-to-digital Converter,
Antenna Aperture,
Bright Line

Biography

Dragana Perkovic has received her Bachelors degree from University of Malta and her doctoral degree from University of Massachusetts, Amherst in 2002 and 2008 respectively, while doing research at the Microwave Remote Sensing Laboratory. She is currently a member of Radar Science and Engineering Section at the Jet Propulsion Laboratory – California Institute of Technology in Pasadena, CA. Her research interests include microwave radar system design, radar remote sensing of the ocean surface and extreme weather phenomena.(Based on document published on 13 May 2013).

Publications

18

Citations

176

Publications by Year

19992013

Co-Authors:

M. Behn
James R. Carswell
P. Chang
Tao Chu
R. Contreras

Show All Co-Authors (28)

Author's Published Works

Search History

Showing 1-25 of 29 results

Conferences (19)

Journals (10)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Qingsen Yan(28)
Yanning Zhang(20)
Yu Zhu(9)
Jinqiu Sun(9)
Dong Gong(8)
Northwestern Polytechnical University(6)
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China(4)
College of Information and Control Engineering, Xi’an University of Architecture and Technology(3)
School of Computer Science and Technology, Xidian University, Xi'an, China(3)
School of Computer Science, The University of Adelaide, Adelaide, SA, Australia(3)
IEEE Transactions on Image Processing(3)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2)
IEEE(29)
Media(2)
New Orleans, LA, USA(4)
Seattle, WA, USA(3)
Vancouver, BC, Canada(3)
Seoul, Korea, Republic of(2)
Hangzhou, China(1)
Optical Flow(14)
Convolutional Layers(13)
High Dynamic Range(13)
Low Dynamic Range(13)
Convolutional Neural Network(12)

Select All on Page

Sort By

Results

From Dynamic to Static: Stepwisely Generate HDR Image for Ghost Removal

Qingsen Yan;Kangzhen Yang;Tao Hu;Genggeng Chen;Kexin Dai;Peng Wu;Wenqi Ren;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Volume: 35, Issue: 2 | Journal Article |

Cited by: Papers (5)

HTML

Generating high-quality high dynamic range (HDR) images in dynamic scenes is particularly challenging due to the influence of large motion. Despite the effectiveness of existing deep learning methods, they still suffer from ghosting artifacts when saturation and motion coexist. Inspired by fusion on static scenes, we propose an inpainting and fusion strategy to enhance the quality of the generated...Show More

From Dynamic to Static: Stepwisely Generate HDR Image for Ghost Removal

Qingsen Yan;Kangzhen Yang;Tao Hu;Genggeng Chen;Kexin Dai;Peng Wu;Wenqi Ren;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Volume: 35, Issue: 2 | Journal Article |

DiffLight: Integrating Content and Detail for Low-light Image Enhancement

Yixu Feng;Shuo Hou;Haotian Lin;Yu Zhu;Peng Wu;Wei Dong;Jinqiu Sun;Qingsen Yan;Yanning Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2024 | Conference Paper |

Cited by: Papers (4)

HTML

The Low Light Image Enhancement (LLIE) task has been a hotspot in low-level computer vision research. The camera sensor can only capture a small amount of ambient light signal in low-light condition, resulting in significant noise black pseudo artifacts in images, which not only degrade visual quality but also affect the performance of down-stream visual tasks. However, current methods often produ...Show More

DiffLight: Integrating Content and Detail for Low-light Image Enhancement

Yixu Feng;Shuo Hou;Haotian Lin;Yu Zhu;Peng Wu;Wei Dong;Jinqiu Sun;Qingsen Yan;Yanning Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2024 | Conference Paper |

Generating Content for HDR Deghosting from Frequency View

Tao Hu;Qingsen Yan;Yuankai Qi;Yanning Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (7)

HTML

Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion. Recent Diffusion Models (DMs) have been introduced in HDR imaging field, demonstrating promising performance, particularly in achieving visually perceptible results compared to previous DNN-based methods. However, DMs ...Show More

Generating Content for HDR Deghosting from Frequency View

Tao Hu;Qingsen Yan;Yuankai Qi;Yanning Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

HL-HDR: Multi-Exposure High Dynamic Range Reconstruction with High-Low Frequency Decomposition

Xiang Zhang;Genggeng Chen;Tao Hu;Kangzhen Yang;Fan Zhang;Qingsen Yan

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

HTML

Generating high-quality High Dynamic Range (HDR) images in dynamic scenes is particularly challenging. Recent Transformer have been introduced in HDR imaging, demonstrating promising performance, particularly in scenarios involving large-scale motion compared to previous CNN-based methods. However, Transformer-based methods face hurdles capturing local details and come with high computational comp...Show More

HL-HDR: Multi-Exposure High Dynamic Range Reconstruction with High-Low Frequency Decomposition

Xiang Zhang;Genggeng Chen;Tao Hu;Kangzhen Yang;Fan Zhang;Qingsen Yan

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

Axi Niu;Trung X. Pham;Kang Zhang;Jinqiu Sun;Yu Zhu;Qingsen Yan;In So Kweon;Yanning Zhang

IEEE Transactions on Broadcasting

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

Cited by: Papers (5)

HTML

Diffusion models have gained significant popularity for image-to-image translation tasks. Previous efforts applying diffusion models to image super-resolution have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinem...Show More

ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

Axi Niu;Trung X. Pham;Kang Zhang;Jinqiu Sun;Yu Zhu;Qingsen Yan;In So Kweon;Yanning Zhang

IEEE Transactions on Broadcasting

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

EiffHDR: An Efficient Network for Multi-Exposure High Dynamic Range Imaging

Xiang Zhang;Qiang Zhu;Tao Hu;Qingsen Yan

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

HTML

While recent progress in Multi-exposure HDR imaging is promising, the growing complexity of state-of-the-art (SOTA) methods poses challenges for their analysis and comparison. In this paper, we analyze the motivations and approaches behind previous SOTA works and introduce EiffHDR, an efficient Multi-exposure HDR imaging technique. In contrast to prior methods employing multiple branches spatial a...Show More

EiffHDR: An Efficient Network for Multi-Exposure High Dynamic Range Imaging

Xiang Zhang;Qiang Zhu;Tao Hu;Qingsen Yan

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Efficient Content Reconstruction for High Dynamic Range Imaging

Xiang Zhang;Tao Hu;Jiashuang He;Qingsen Yan

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

HTML

High Dynamic Range (HDR) images can be reconstructed from multiple Low Dynamic Range (LDR) images using existing deep neural network (DNN) techniques. Despite notable advancements, DNN-based methods still exhibit ghosting artifacts when handling LDR images with saturation and significant motion. Recent Diffusion models (DMs) have been introduced in HDR imaging, showcasing promising performance, es...Show More

Efficient Content Reconstruction for High Dynamic Range Imaging

Xiang Zhang;Tao Hu;Jiashuang He;Qingsen Yan

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Toward High-Quality HDR Deghosting With Conditional Diffusion Models

Qingsen Yan;Tao Hu;Yuan Sun;Hao Tang;Yu Zhu;Wei Dong;Luc Van Gool;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

Cited by: Papers (32)

HTML

High Dynamic Range (HDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. Despite the remarkable progress, DNN-based methods still generate ghosting artifacts when LDR images have saturation and large motion, which hinders potential applications in real-world scenarios. To address this challenge, we formulate the HDR deghosting...Show More

Toward High-Quality HDR Deghosting With Conditional Diffusion Models

Qingsen Yan;Tao Hu;Yuan Sun;Hao Tang;Yu Zhu;Wei Dong;Luc Van Gool;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

Self-Supervised Interactive Embedding for One-Shot Organ Segmentation

Yang Yang;Bo Wang;Dingwen Zhang;Yixuan Yuan;Qingsen Yan;Shijie Zhao;Zheng You;Junwei Han

IEEE Transactions on Biomedical Engineering

Year: 2023 | Volume: 70, Issue: 10 | Journal Article |

Cited by: Papers (7)

HTML

One-shot organ segmentation (OS2) aims at segmenting the desired organ regions from the input medical imaging data with only one pre-annotated example as the reference. By using the minimal annotation data to facilitate organ segmentation, OS2 receives great attention in the medical image analysis community due to its weak requirement on human annotation. In OS2, one core issue is to explore the m...Show More

Self-Supervised Interactive Embedding for One-Shot Organ Segmentation

Yang Yang;Bo Wang;Dingwen Zhang;Yixuan Yuan;Qingsen Yan;Shijie Zhao;Zheng You;Junwei Han

IEEE Transactions on Biomedical Engineering

Year: 2023 | Volume: 70, Issue: 10 | Journal Article |

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

Qingsen Yan;Song Zhang;Weiye Chen;Hao Tang;Yu Zhu;Jinqiu Sun;Luc Van Gool;Yanning Zhang

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

Year: 2023 | Conference Paper |

Cited by: Papers (7)

HTML

Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNN...Show More

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

Qingsen Yan;Song Zhang;Weiye Chen;Hao Tang;Yu Zhu;Jinqiu Sun;Luc Van Gool;Yanning Zhang

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

Year: 2023 | Conference Paper |

A Unified HDR Imaging Method with Pixel and Patch Level

Qingsen Yan;Weiye Chen;Song Zhang;Yu Zhu;Jinqiu Sun;Yanning Zhang

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

Year: 2023 | Conference Paper |

Cited by: Papers (21)

HTML

Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To ge...Show More

A Unified HDR Imaging Method with Pixel and Patch Level

Qingsen Yan;Weiye Chen;Song Zhang;Yu Zhu;Jinqiu Sun;Yanning Zhang

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

Year: 2023 | Conference Paper |

Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Marcos V. Conde;Eduard Zamfir;Radu Timofte;Daniel Motilla;Cen Liu;Zexin Zhang;Yunbo Peng;Yue Lin;Jiaming Guo;Xueyi Zou;Yuyi Chen;Yi Liu;Jia Hao;Youliang Yan;Yuanfan Zhang;Gen Li;Lei Sun;Lingshun Kong;Haoran Bai;Jinshan Pan;Jiangxin Dong;Jinhui Tang;Mustafa Ayazoglu;Bahri Batuhan Bilecen;Mingxi Li;Yuhang Zhang;Xianjun Fan;Yankai Sheng;Long Sun;Zibin Liu;Weiran Gou;Shaoqing Li;Ziyao Yi;Yan Xiang;Dehui Kong;Ke Xu;Ganzorig Gankhuyag;Kihwan Yoon;Jin Zhang;Gaocheng Yu;Feng Zhang;Hongbin Wang;Zhou Zhou;Jiahao Chao;Hongfan Gao;Jiali Gong;Zhengfeng Yang;Zhenbing Zeng;Chengpeng Chen;Zichao Guo;Anjin Park;Yuqing Liu;Qi Jia;Hongyuan Yu;Xuanwu Yin;Kunlong Zuo;Dongyang Zhang;Ting Fu;Zhengxue Cheng;Shiai Zhu;Dajiang Zhou;Hongyuan Yu;Weichen Yu;Lin Ge;Jiahua Dong;Yajun Zou;Zhuoyuan Wu;Binnan Han;Xiaolin Zhang;Heng Zhang;Xuanwu Yin;Ben Shao;Shaolong Zheng;Daheng Yin;Baijun Chen;Mengyang Liu;Marian-Sergiu Nistor;Yi-Chung Chen;Zhi-Kai Huang;Yuan-Chun Chiang;Wei-Ting Chen;Hao-Hsiang Yang;Hua-En Chang;I-Hsiang Chen;Chia-Hsuan Hsieh;Sy-Yen Kuo;Tu Vo;Qingsen Yan;Yun Zhu;Jinqiu Su;Yanning Zhang;Cheng Zhang;Jiaying Luo;Youngsun Cho;Nakyung Lee;Kunlong Zuo

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

Year: 2023 | Conference Paper |

Cited by: Papers (20)

HTML

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the me...Show More

Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Marcos V. Conde;Eduard Zamfir;Radu Timofte;Daniel Motilla;Cen Liu;Zexin Zhang;Yunbo Peng;Yue Lin;Jiaming Guo;Xueyi Zou;Yuyi Chen;Yi Liu;Jia Hao;Youliang Yan;Yuanfan Zhang;Gen Li;Lei Sun;Lingshun Kong;Haoran Bai;Jinshan Pan;Jiangxin Dong;Jinhui Tang;Mustafa Ayazoglu;Bahri Batuhan Bilecen;Mingxi Li;Yuhang Zhang;Xianjun Fan;Yankai Sheng;Long Sun;Zibin Liu;Weiran Gou;Shaoqing Li;Ziyao Yi;Yan Xiang;Dehui Kong;Ke Xu;Ganzorig Gankhuyag;Kihwan Yoon;Jin Zhang;Gaocheng Yu;Feng Zhang;Hongbin Wang;Zhou Zhou;Jiahao Chao;Hongfan Gao;Jiali Gong;Zhengfeng Yang;Zhenbing Zeng;Chengpeng Chen;Zichao Guo;Anjin Park;Yuqing Liu;Qi Jia;Hongyuan Yu;Xuanwu Yin;Kunlong Zuo;Dongyang Zhang;Ting Fu;Zhengxue Cheng;Shiai Zhu;Dajiang Zhou;Hongyuan Yu;Weichen Yu;Lin Ge;Jiahua Dong;Yajun Zou;Zhuoyuan Wu;Binnan Han;Xiaolin Zhang;Heng Zhang;Xuanwu Yin;Ben Shao;Shaolong Zheng;Daheng Yin;Baijun Chen;Mengyang Liu;Marian-Sergiu Nistor;Yi-Chung Chen;Zhi-Kai Huang;Yuan-Chun Chiang;Wei-Ting Chen;Hao-Hsiang Yang;Hua-En Chang;I-Hsiang Chen;Chia-Hsuan Hsieh;Sy-Yen Kuo;Tu Vo;Qingsen Yan;Yun Zhu;Jinqiu Su;Yanning Zhang;Cheng Zhang;Jiaying Luo;Youngsun Cho;Nakyung Lee;Kunlong Zuo

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

Year: 2023 | Conference Paper |

SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring

Qingsen Yan;Dong Gong;Pei Wang;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Cited by: Papers (9)

HTML

The goal of dynamic scene deblurring is to remove the motion blur presented in a given image. To recover the details from the severe blurs, conventional convolutional neural networks (CNNs) based methods typically increase the number of convolution layers, kernel-size, or different scale images to enlarge the receptive field. However, these methods neglect the non-uniform nature of blurs, and cann...Show More

SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring

Qingsen Yan;Dong Gong;Pei Wang;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation

Ruiyang Li;Longchang Xu;Kun Xie;Jianfeng Song;Xiaowen Ma;Liang Chang;Qingsen Yan

IEEE Journal of Biomedical and Health Informatics

Year: 2023 | Volume: 27, Issue: 7 | Journal Article |

Cited by: Papers (13)

HTML

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However...Show More

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation

Ruiyang Li;Longchang Xu;Kun Xie;Jianfeng Song;Xiaowen Ma;Liang Chang;Qingsen Yan

IEEE Journal of Biomedical and Health Informatics

Year: 2023 | Volume: 27, Issue: 7 | Journal Article |

Exploring and Evaluating Image Restoration Potential in Dynamic Scenes

Cheng Zhang;Shaolin Su;Yu Zhu;Qingsen Yan;Jinqiu Sun;Yanning Zhang

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

Year: 2022 | Conference Paper |

Cited by: Papers (8)

HTML

In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obt...Show More

Exploring and Evaluating Image Restoration Potential in Dynamic Scenes

Cheng Zhang;Shaolin Su;Yu Zhu;Qingsen Yan;Jinqiu Sun;Yanning Zhang

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

Year: 2022 | Conference Paper |

Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning

Qingsen Yan;Dong Gong;Yuhang Liu;Anton van den Hengel;Javen Qinfeng Shi

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

Year: 2022 | Conference Paper |

Cited by: Papers (17)

HTML

Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks whi...Show More

Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning

Qingsen Yan;Dong Gong;Yuhang Liu;Anton van den Hengel;Javen Qinfeng Shi

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

Year: 2022 | Conference Paper |

A Lightweight Network for High Dynamic Range Imaging

Qingsen Yan;Song Zhang;Weiye Chen;Yuhang Liu;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi;Dong Gong

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

Year: 2022 | Conference Paper |

Cited by: Papers (11)

HTML

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of illuminance with differently exposed images. They suffer from ghost artifacts when camera jittering or object moving. Several methods can generate high-quality HDR images with high computational complexity, but the inference process is too slow. However, the network with small parameters will produce unsatisfact...Show More

A Lightweight Network for High Dynamic Range Imaging

Qingsen Yan;Song Zhang;Weiye Chen;Yuhang Liu;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi;Dong Gong

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

Year: 2022 | Conference Paper |

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

Eduardo Pérez-Pellitero;Sibi Catley-Chandar;Richard Shaw;Aleš Leonardis;Radu Timofte;Zexin Zhang;Cen Liu;Yunbo Peng;Yue Lin;Gaocheng Yu;Jin Zhang;Zhe Ma;Hongbin Wang;Xiangyu Chen;Xintao Wang;Haiwei Wu;Lin Liu;Chao Dong;Jiantao Zhou;Qingsen Yan;Song Zhang;Weiye Chen;Yuhang Liu;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi;Dong Gong;Dan Zhu;Mengdi Sun;Guannan Chen;Yang Hu;Haowei Li;Baozhu Zou;Zhen Liu;Wenjie Lin;Ting Jiang;Chengzhi Jiang;Xinpeng Li;Mingyan Han;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Juan Marín-Vega;Michael Sloth;Peter Schneider-Kamp;Richard Röttger;Chunyang Li;Long Bao;Gang He;Ziyao Xu;Li Xu;Gen Zhan;Ming Sun;Xing Wen;Junlin Li;Jinjing Li;Chenghua Li;Ruipeng Gang;Fangya Li;Chenming Liu;Shuang Feng;Fei Lei;Rui Liu;Junxiang Ruan;Tianhong Dai;Wei Li;Zhan Lu;Hengyan Liu;Peian Huang;Guangyu Ren;Yonglin Luo;Chang Liu;Qiang Tu;Fangya Li;Ruipeng Gang;Chenghua Li;Jinjing Li;Sai Ma;Chenming Liu;Yizhen Cao;Steven Tel;Barthelemy Heyrman;Dominique Ginhac;Chul Lee;Gahyeon Kim;Seonghyun Park;An Gia Vien;Truong Thanh Nhat Mai;Howoon Yoon;Tu Vo;Alexander Holston;Sheir Zaheer;Chan Y. Park

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

Year: 2022 | Conference Paper |

Cited by: Papers (29)

HTML

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) ...Show More

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

Eduardo Pérez-Pellitero;Sibi Catley-Chandar;Richard Shaw;Aleš Leonardis;Radu Timofte;Zexin Zhang;Cen Liu;Yunbo Peng;Yue Lin;Gaocheng Yu;Jin Zhang;Zhe Ma;Hongbin Wang;Xiangyu Chen;Xintao Wang;Haiwei Wu;Lin Liu;Chao Dong;Jiantao Zhou;Qingsen Yan;Song Zhang;Weiye Chen;Yuhang Liu;Zhen Zhang;Yanning Zhang;Javen Qinfeng Shi;Dong Gong;Dan Zhu;Mengdi Sun;Guannan Chen;Yang Hu;Haowei Li;Baozhu Zou;Zhen Liu;Wenjie Lin;Ting Jiang;Chengzhi Jiang;Xinpeng Li;Mingyan Han;Haoqiang Fan;Jian Sun;Shuaicheng Liu;Juan Marín-Vega;Michael Sloth;Peter Schneider-Kamp;Richard Röttger;Chunyang Li;Long Bao;Gang He;Ziyao Xu;Li Xu;Gen Zhan;Ming Sun;Xing Wen;Junlin Li;Jinjing Li;Chenghua Li;Ruipeng Gang;Fangya Li;Chenming Liu;Shuang Feng;Fei Lei;Rui Liu;Junxiang Ruan;Tianhong Dai;Wei Li;Zhan Lu;Hengyan Liu;Peian Huang;Guangyu Ren;Yonglin Luo;Chang Liu;Qiang Tu;Fangya Li;Ruipeng Gang;Chenghua Li;Jinjing Li;Sai Ma;Chenming Liu;Yizhen Cao;Steven Tel;Barthelemy Heyrman;Dominique Ginhac;Chul Lee;Gahyeon Kim;Seonghyun Park;An Gia Vien;Truong Thanh Nhat Mai;Howoon Yoon;Tu Vo;Alexander Holston;Sheir Zaheer;Chan Y. Park

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

Year: 2022 | Conference Paper |

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data

Bo Wang;Qinzsen Yan;Zhengqing Xu;Jingyang Ai;Shuo Jin;Wei Xu;Wei Zhao;Liang Zhang;Zheng You

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

HTML

How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and the quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216...Show More

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data

Bo Wang;Qinzsen Yan;Zhengqing Xu;Jingyang Ai;Shuo Jin;Wei Xu;Wei Zhao;Liang Zhang;Zheng You

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations

Qingsen Yan;Bo Wang;Dong Gong;Chuan Luo;Wei Zhao;Jianhu Shen;Jingyang Ai;Qinfeng Shi;Yanning Zhang;Shuo Jin;Liang Zhang;Zheng You

IEEE Transactions on Big Data

Year: 2021 | Volume: 7, Issue: 1 | Journal Article |

Cited by: Papers (72)

HTML

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirem...Show More

COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations

Qingsen Yan;Bo Wang;Dong Gong;Chuan Luo;Wei Zhao;Jianhu Shen;Jingyang Ai;Qinfeng Shi;Yanning Zhang;Shuo Jin;Liang Zhang;Zheng You

IEEE Transactions on Big Data

Year: 2021 | Volume: 7, Issue: 1 | Journal Article |

Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation

Qingsen Yan;Bo Wang;Wei Zhang;Chuan Luo;Wei Xu;Zhengqing Xu;Yanning Zhang;Qinfeng Shi;Liang Zhang;Zheng You

IEEE Journal of Biomedical and Health Informatics

Year: 2021 | Volume: 25, Issue: 7 | Journal Article |

Cited by: Papers (79)

HTML

Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical planning of liver diseases. In clinical practice, liver vessels are normally manual annotated by clinicians on each slice of CT images, which is extremely laborious. Several deep learning methods exist for liver vessel segmentation, however, promoting the performance of segmentation remains a major challenge ...Show More

Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation

Qingsen Yan;Bo Wang;Wei Zhang;Chuan Luo;Wei Xu;Zhengqing Xu;Yanning Zhang;Qinfeng Shi;Liang Zhang;Zheng You

IEEE Journal of Biomedical and Health Informatics

Year: 2021 | Volume: 25, Issue: 7 | Journal Article |

Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Yu Liu;Lingqiao Liu;Haokui Zhang;Hamid Rezatofighi;Qingsen Yan;Ian Reid

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Cited by: Papers (7)

HTML

Video object segmentation plays a vital role to many robotic tasks, beyond the satisfied accuracy, quickly adapt to the new scenario with very limited annotations and conduct a quick inference are also important. In this paper, we are specifically concerned with the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such ann...Show More

Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Yu Liu;Lingqiao Liu;Haokui Zhang;Hamid Rezatofighi;Qingsen Yan;Ian Reid

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network

Shaolin Su;Qingsen Yan;Yu Zhu;Cheng Zhang;Xin Ge;Jinqiu Sun;Yanning Zhang

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

Year: 2020 | Conference Paper |

Cited by: Papers (380)

HTML

Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to real-world distorted images. To deal with the challenge, we propose a self-adaptiv...Show More

Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network

Shaolin Su;Qingsen Yan;Yu Zhu;Cheng Zhang;Xin Ge;Jinqiu Sun;Yanning Zhang

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

Year: 2020 | Conference Paper |

Attention-Based Network For Low-Light Image Enhancement

Cheng Zhang;Qingsen Yan;Yu Zhu;Xianjun Li;Jinqiu Sun;Yanning Zhang

2020 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2020 | Conference Paper |

Cited by: Papers (30)

HTML

The captured images under low-light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vision. A variety of methods have been proposed for this task, but these methods often failed in an extreme low-light environment and amplified the underlying noise in the input image. To address such a difficult problem, ...Show More

Attention-Based Network For Low-Light Image Enhancement

Cheng Zhang;Qingsen Yan;Yu Zhu;Xianjun Li;Jinqiu Sun;Yanning Zhang

2020 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2020 | Conference Paper |

Deep HDR Imaging via A Non-Local Network

Qingsen Yan;Lei Zhang;Yu Liu;Yu Zhu;Jinqiu Sun;Qinfeng Shi;Yanning Zhang

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Cited by: Papers (138)

HTML

One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through aligning LDR inputs based on optical flow or detecting anomalies among them. Howeve...Show More

Deep HDR Imaging via A Non-Local Network

Qingsen Yan;Lei Zhang;Yu Liu;Yu Zhu;Jinqiu Sun;Qinfeng Shi;Yanning Zhang

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

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.