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Pheng-Ann Heng

Also published under: Pheng Ann Heng, P. -A. Heng, P. A. Heng, Pheng A. Heng, Pheng–Ann Heng, Phena-Ann Heng, P. Heng

Publications
225
Citations
14,772
Publications by Year
19902025
Co-Authors:
Show All Co-Authors (642)
Image of author Pheng-Ann Heng

Pheng-Ann Heng

Also published under: Pheng Ann Heng, P. -A. Heng, P. A. Heng, Pheng A. Heng, Pheng–Ann Heng, Phena-Ann Heng, P. Heng

Affiliation

Department of Computer Science and Engineering
Chinese University of Hong Kong
Hong Kong, China

Publication Topics

Biography

Pheng Ann Heng (Senior Member, IEEE) received the Ph.D. degree in computer science from Indiana University, Bloomington, IN, USA.,He is currently a Professor with the Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, where he is also the Director of the Virtual Reality, Visualization, and Imaging Research Centre. He is also the Director of the Research Center for Human–Computer Interaction, Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, Shenzhen, China. His research interests include VR and AI applications in medicine, visualization, medical imaging, human–computer interfaces, and interactive graphics.(Based on document published on 23 September 2020).
Publications
225
Citations
14,772
Publications by Year
19902025
Co-Authors:
Show All Co-Authors (642)
Author's Published Works

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Towards Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

Long Lei;Yingbai Hu;Zixing Jiang;Juzheng Miao;Xiao Luo;Yu Zhang;Qiong Wang;Shujun Wang;Zheng Li;Pheng-Ann Heng

IEEE Transactions on Medical Robotics and Bionics
Year: 2025 | Early Access Article |
Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors....Show More

Towards Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

Long Lei;Yingbai Hu;Zixing Jiang;Juzheng Miao;Xiao Luo;Yu Zhang;Qiong Wang;Shujun Wang;Zheng Li;Pheng-Ann Heng

Year: 2025 | Early Access Article |

Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects

Shi Qiu;Binzhu Xie;Qixuan Liu;Pheng-Ann Heng

2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)
Year: 2025 | Conference Paper |
3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for XR, few have explicitly focused on or demonstrated its effectiveness within XR en...Show More

Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects

Shi Qiu;Binzhu Xie;Qixuan Liu;Pheng-Ann Heng

Year: 2025 | Conference Paper |

Multi-organ Segmentation from Partially Labeled and Unaligned Multi-modal MRI in Thyroid-associated Orbitopathy

Cheng Chen;Min Deng;Yuan Zhong;Jinyue Cai;Karen Kar Wun Chan;Qi Dou;Kelvin Kam Lung Chong;Pheng-Ann Heng;Winnie Chiu-Wing Chu

IEEE Journal of Biomedical and Health Informatics
Year: 2025 | Early Access Article |
Thyroid-associated orbitopathy (TAO) is a prevalent inflammatory autoimmune disorder, leading to orbital disfigurement and visual disability. Automatic comprehensive segmentation tailored for quantitative multi-modal MRI assessment of TAO holds enormous promise but is still lacking. In this paper, we propose a novel method, named cross-modal attentive self-training (CMAST), for the multi-organ seg...Show More

Multi-organ Segmentation from Partially Labeled and Unaligned Multi-modal MRI in Thyroid-associated Orbitopathy

Cheng Chen;Min Deng;Yuan Zhong;Jinyue Cai;Karen Kar Wun Chan;Qi Dou;Kelvin Kam Lung Chong;Pheng-Ann Heng;Winnie Chiu-Wing Chu

Year: 2025 | Early Access Article |

Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild

Yu Zhou;Jialun Pei;Weixin Si;Jing Qin;Pheng-Ann Heng

IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
The Facial Expression Recognition (FER) technique has increasingly matured over time. However, recognizing facial expressions in wild environments poses great challenges in achieving promising performance. The main obstacles arise from various factors, such as illumination changes, head pose variations, and occlusions. To overcome interferences from external environments and improve recognition ac...Show More

Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild

Yu Zhou;Jialun Pei;Weixin Si;Jing Qin;Pheng-Ann Heng

Year: 2025 | Early Access Article |

Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning

Yuchen Yuan;Xi Wang;Xikai Yang;Pheng-Ann Heng

IEEE Transactions on Medical Imaging
Year: 2025 | Volume: 44, Issue: 3 | Journal Article |
Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous literature. To address this issue, we propose a probabilistic prototype-based classifier that introduces uncertainty estimation into the entire pixel cla...Show More

Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning

Yuchen Yuan;Xi Wang;Xikai Yang;Pheng-Ann Heng

Year: 2025 | Volume: 44, Issue: 3 | Journal Article |

DC²T: Disentanglement-Guided Consolidation and Consistency Training for Semi-Supervised Cross-Site Continual Segmentation

Jingyang Zhang;Jialun Pei;Dunyuan Xu;Yueming Jin;Pheng-Ann Heng

IEEE Transactions on Medical Imaging
Year: 2025 | Volume: 44, Issue: 2 | Journal Article |
Continual Learning (CL) is recognized to be a storage-efficient and privacy-protecting approach for learning from sequentially-arriving medical sites. However, most existing CL methods assume that each site is fully labeled, which is impractical due to budget and expertise constraint. This paper studies the Semi-Supervised Continual Learning (SSCL) that adopts partially-labeled sites arriving over...Show More

DC²T: Disentanglement-Guided Consolidation and Consistency Training for Semi-Supervised Cross-Site Continual Segmentation

Jingyang Zhang;Jialun Pei;Dunyuan Xu;Yueming Jin;Pheng-Ann Heng

Year: 2025 | Volume: 44, Issue: 2 | Journal Article |

S²Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR

Jialun Pei;Diandian Guo;Jingyang Zhang;Manxi Lin;Yueming Jin;Pheng-Ann Heng

IEEE Transactions on Medical Imaging
Year: 2025 | Volume: 44, Issue: 1 | Journal Article |
Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR). However, previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. This pipeline may potentially compromise the flexibility of learning mu...Show More

S²Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR

Jialun Pei;Diandian Guo;Jingyang Zhang;Manxi Lin;Yueming Jin;Pheng-Ann Heng

Year: 2025 | Volume: 44, Issue: 1 | Journal Article |

An Adaptive and Interpretable Congestion Control Service Based on Multi-Objective Reinforcement Learning

Jiacheng Liu;Xu Li;Feilong Tang;Peng Li;Long Chen;Jiadi Yu;Yanmin Zhu;Pheng-Ann Heng;Laurence T. Yang

IEEE Transactions on Services Computing
Year: 2024 | Early Access Article |
The need for an adaptive congestion control (CC) service is crucial due to the heterogeneity of systems and the diversity of applications. Traditional CC methods often fail to adaptively balance throughput and delay, struggling to meet the varied demands of different network applications. In this work, we introduce Auto, a novel CC service that employs Multi-Objective Reinforcement Learning (MORL)...Show More

An Adaptive and Interpretable Congestion Control Service Based on Multi-Objective Reinforcement Learning

Jiacheng Liu;Xu Li;Feilong Tang;Peng Li;Long Chen;Jiadi Yu;Yanmin Zhu;Pheng-Ann Heng;Laurence T. Yang

Year: 2024 | Early Access Article |

Video Instance Shadow Detection Under the Sun and Sky

Zhenghao Xing;Tianyu Wang;Xiaowei Hu;Haoran Wu;Chi-Wing Fu;Pheng-Ann Heng

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Instance shadow detection, crucial for applications such as photo editing and light direction estimation, has undergone significant advancements in predicting shadow instances, object instances, and their associations. The extension of this task to videos presents challenges in annotating diverse video data and addressing complexities arising from occlusion and temporary disappearances within asso...Show More

Video Instance Shadow Detection Under the Sun and Sky

Zhenghao Xing;Tianyu Wang;Xiaowei Hu;Haoran Wu;Chi-Wing Fu;Pheng-Ann Heng

Year: 2024 | Volume: 33 | Journal Article |

G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

Haoxin Yang;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Jing Qin;Yi Wang;Pheng-Ann Heng;Shengfeng He

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent man...Show More

G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

Haoxin Yang;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Jing Qin;Yi Wang;Pheng-Ann Heng;Shengfeng He

Year: 2024 | Volume: 19 | Journal Article |

CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation

Jialun Pei;Tao Jiang;He Tang;Nian Liu;Yueming Jin;Deng-Ping Fan;Pheng-Ann Heng

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Cited by: Papers (3)
In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weigh...Show More

CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation

Jialun Pei;Tao Jiang;He Tang;Nian Liu;Yueming Jin;Deng-Ping Fan;Pheng-Ann Heng

Year: 2024 | Volume: 33 | Journal Article |

Uncertainty-Aware Suction Grasping for Cluttered Scenes

Rui Cao;Biqi Yang;Yichuan Li;Chi-Wing Fu;Pheng-Ann Heng;Yun-Hui Liu

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 6 | Journal Article |
In this work, we present a multi-stage pipeline that aims to accurately predict suction grasps for objects with varying properties in cluttered and complex scenes. Existing methods face difficulties in generalizing to unseen objects and effectively handling noisy depth/point cloud data, which often leads to inaccurate grasp predictions. To address these challenges, we utilize the Unseen Object Ins...Show More

Uncertainty-Aware Suction Grasping for Cluttered Scenes

Rui Cao;Biqi Yang;Yichuan Li;Chi-Wing Fu;Pheng-Ann Heng;Yun-Hui Liu

Year: 2024 | Volume: 9, Issue: 6 | Journal Article |

PPN-Pack: Placement Proposal Network for Efficient Robotic Bin Packing

Jia-Hui Pan;Xiaojie Gao;Ka-Hei Hui;Shize Zhu;Yun-Hui Liu;Pheng-Ann Heng;Chi-Wing Fu

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 6 | Journal Article |
Cited by: Papers (1)
Robotic bin packing is a challenging task, requiring compactly packing objects in a container and also efficiently performing the computation, such that the robot arm need not wait too long before taking action. In this work, we introduce PPN-Pack, a novel learning-based approach to improve the efficiency of packing general objects. Our key idea is to learn to predict good placement locations for ...Show More

PPN-Pack: Placement Proposal Network for Efficient Robotic Bin Packing

Jia-Hui Pan;Xiaojie Gao;Ka-Hei Hui;Shize Zhu;Yun-Hui Liu;Pheng-Ann Heng;Chi-Wing Fu

Year: 2024 | Volume: 9, Issue: 6 | Journal Article |

Semantic-Oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images

Yuhan Zhang;Xiao Ma;Kun Huang;Mingchao Li;Pheng-Ann Heng

IEEE Transactions on Medical Imaging
Year: 2024 | Volume: 43, Issue: 8 | Journal Article |
Cited by: Papers (2)
Diabetic retinopathy (DR) is a serious ocular condition that requires effective monitoring and treatment by ophthalmologists. However, constructing a reliable DR grading model remains a challenging and costly task, heavily reliant on high-quality training sets and adequate hardware resources. In this paper, we investigate the knowledge transferability of large-scale pre-trained models (LPMs) to fu...Show More

Semantic-Oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images

Yuhan Zhang;Xiao Ma;Kun Huang;Mingchao Li;Pheng-Ann Heng

Year: 2024 | Volume: 43, Issue: 8 | Journal Article |

A Dual Enrichment Synergistic Strategy to Handle Data Heterogeneity for Domain Incremental Cardiac Segmentation

Kang Li;Yu Zhu;Lequan Yu;Pheng-Ann Heng

IEEE Transactions on Medical Imaging
Year: 2024 | Volume: 43, Issue: 6 | Journal Article |
Cited by: Papers (2)
Upon remarkable progress in cardiac image segmentation, contemporary studies dedicate to further upgrading model functionality toward perfection, through progressively exploring the sequentially delivered datasets over time by domain incremental learning. Existing works mainly concentrated on addressing the heterogeneous style variations, but overlooked the critical shape variations across domains...Show More

A Dual Enrichment Synergistic Strategy to Handle Data Heterogeneity for Domain Incremental Cardiac Segmentation

Kang Li;Yu Zhu;Lequan Yu;Pheng-Ann Heng

Year: 2024 | Volume: 43, Issue: 6 | Journal Article |

A Survey on Generative Diffusion Models

Hanqun Cao;Cheng Tan;Zhangyang Gao;Yilun Xu;Guangyong Chen;Pheng-Ann Heng;Stan Z. Li

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 7 | Journal Article |
Cited by: Papers (60)
Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassi...Show More

A Survey on Generative Diffusion Models

Hanqun Cao;Cheng Tan;Zhangyang Gao;Yilun Xu;Guangyong Chen;Pheng-Ann Heng;Stan Z. Li

Year: 2024 | Volume: 36, Issue: 7 | Journal Article |

Deep Omni-Supervised Learning for Rib Fracture Detection From Chest Radiology Images

Zhizhong Chai;Luyang Luo;Huangjing Lin;Pheng-Ann Heng;Hao Chen

IEEE Transactions on Medical Imaging
Year: 2024 | Volume: 43, Issue: 5 | Journal Article |
Cited by: Papers (3)
Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based object detection models requires a huge amount of bounding box annotation. However, annotating medical data is time-consuming and expertise-demanding, making obtaining a large amount of fine-grained annotations extremely...Show More

Deep Omni-Supervised Learning for Rib Fracture Detection From Chest Radiology Images

Zhizhong Chai;Luyang Luo;Huangjing Lin;Pheng-Ann Heng;Hao Chen

Year: 2024 | Volume: 43, Issue: 5 | Journal Article |

A Simultaneous Polyp and Lumen Detection Framework Toward Autonomous Robotic Colonoscopy

Wing Yin Ng;Yehui Li;Tianle Pan;Yichong Sun;Qi Dou;Pheng Ann Heng;Philip Wai Yan Chiu;Zheng Li

IEEE Transactions on Medical Robotics and Bionics
Year: 2024 | Volume: 6, Issue: 1 | Journal Article |
Cited by: Papers (2)
Colorectal Cancer is one of the deadliest diseases with a high incidence and mortality worldwide. Robotic colonoscopes have been extensively developed to provide alternative solutions for colon screening. Nevertheless, most robotic colonoscopes remain a low autonomy level, which leads to non-intuitive manipulation and limits their clinical translation. This paper proposes a deep learning-based fra...Show More

A Simultaneous Polyp and Lumen Detection Framework Toward Autonomous Robotic Colonoscopy

Wing Yin Ng;Yehui Li;Tianle Pan;Yichong Sun;Qi Dou;Pheng Ann Heng;Philip Wai Yan Chiu;Zheng Li

Year: 2024 | Volume: 6, Issue: 1 | Journal Article |

Structural and Pixel Relation Modeling for Semisupervised Instrument Segmentation From Surgical Videos

Caizi Li;Yaoqian Li;Ruiqiang Liu;Guangsuo Wang;Jianping Lv;Yueming Jin;Weixin Si;Pheng-Ann Heng

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (2)
Automatic instrument segmentation from surgical videos via deep learning has drawn increasing attention recently. However, interferences, such as blood or illumination, induce confusion of targets, which can be further exacerbated by the lack of labeled data, making accurate segmentation of instruments very challenging. Previous methods rarely pay attention to analyze confusion regions. In this ar...Show More

Structural and Pixel Relation Modeling for Semisupervised Instrument Segmentation From Surgical Videos

Caizi Li;Yaoqian Li;Ruiqiang Liu;Guangsuo Wang;Jianping Lv;Yueming Jin;Weixin Si;Pheng-Ann Heng

Year: 2024 | Volume: 73 | Journal Article |

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

Long Lei;Baoliang Zhao;Xiaozhi Qi;Rui Mi;Hai Ye;Peng Zhang;Qiong Wang;Pheng-Ann Heng;Ying Hu

IEEE Transactions on Automation Science and Engineering
Year: 2024 | Volume: 21, Issue: 4 | Journal Article |
Cited by: Papers (4)
Puncture robots pave a new way for stable, accurate and safe percutaneous liver tumor puncture operation. However, affected by respiratory motion, intraoperative accurate location of the tumor and its surrounding anatomical structures remains a difficult problem in existing robot-assisted puncture operations. In this paper, a dual-arm robotic needle insertion system with guidance of intraoperative...Show More

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

Long Lei;Baoliang Zhao;Xiaozhi Qi;Rui Mi;Hai Ye;Peng Zhang;Qiong Wang;Pheng-Ann Heng;Ying Hu

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Kine-Appendage: Enhancing Freehand VR Interaction Through Transformations of Virtual Appendages

Yang Tian;Hualong Bai;Shengdong Zhao;Chi-Wing Fu;Chun Yu;Haozhao Qin;Qiong Wang;Pheng-Ann Heng

IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Volume: 30, Issue: 7 | Journal Article |
Cited by: Papers (5)
Kinesthetic feedback, the feeling of restriction or resistance when hands contact objects, is essential for natural freehand interaction in VR. However, inducing kinesthetic feedback using mechanical hardware can be cumbersome and hard to control in commodity VR systems. We propose the kine-appendage concept to compensate for the loss of kinesthetic feedback in virtual environments, i.e., a virtua...Show More

Kine-Appendage: Enhancing Freehand VR Interaction Through Transformations of Virtual Appendages

Yang Tian;Hualong Bai;Shengdong Zhao;Chi-Wing Fu;Chun Yu;Haozhao Qin;Qiong Wang;Pheng-Ann Heng

Year: 2024 | Volume: 30, Issue: 7 | Journal Article |

LVCL: Label-Volume Contrastive Learning for Multi-Label Classification of Retinal Oct Volumes

Yuhan Zhang;Ziqi Tang;Dawei Yang;An-Ran Ran;Carol Y. Cheung;Pheng-Ann Heng

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Optical coherence tomography (OCT) technique can produce volumetric data of the retina for disease diagnosis. Each OCT volume consists of 2D B-scans, and recent studies have shown remarkable success with deep learning for single-label B-scan classification tasks. However, B-scan annotation is quite difficult and single-label classification approaches cannot meet the growing clinical demands. It is...Show More

LVCL: Label-Volume Contrastive Learning for Multi-Label Classification of Retinal Oct Volumes

Yuhan Zhang;Ziqi Tang;Dawei Yang;An-Ran Ran;Carol Y. Cheung;Pheng-Ann Heng

Year: 2023 | Conference Paper |

RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

Donghao Zhou;Chunbin Gu;Junde Xu;Furui Liu;Qiong Wang;Guangyong Chen;Pheng-Ann Heng

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unf...Show More

RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

Donghao Zhou;Chunbin Gu;Junde Xu;Furui Liu;Qiong Wang;Guangyong Chen;Pheng-Ann Heng

Year: 2023 | Conference Paper |

Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition

Zhipeng Zhou;Lanqing Li;Peilin Zhao;Pheng-Ann Heng;Wei Gong

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly imbalanced label distribution. In this paper, through empirical observations, we a...Show More

Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition

Zhipeng Zhou;Lanqing Li;Peilin Zhao;Pheng-Ann Heng;Wei Gong

Year: 2023 | Conference Paper |

Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Jiaqi Xu;Xiaowei Hu;Lei Zhu;Qi Dou;Jifeng Dai;Yu Qiao;Pheng-Ann Heng

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (17)
Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery modu...Show More

Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Jiaqi Xu;Xiaowei Hu;Lei Zhu;Qi Dou;Jifeng Dai;Yu Qiao;Pheng-Ann Heng

Year: 2023 | Conference Paper |

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