Runhua Wang - IEEE Xplore Author Profile

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Author details

Runhua Wang

Publications

17

Citations

123

Publications by Year

20192024

Co-Authors:

Qingchen BiYongchun FangHaiming GaoBaoquan LiChenping Li

Show All Co-Authors (24)

Runhua Wang

Affiliation

Tianjin Key Laboratory of Intelligent Robotics

Nankai University

Tianjin, China

Publication Topics

Path Planning,
Feature Points,
Mobile Robot,
Rapidly-exploring Random Tree,
Simultaneous Localization And Mapping,
Point Cloud,
Pose Estimation,
Robot Pose,
Visual Servoing,
Camera Field Of View,
Exploration Time,
Information Gain

Biography

Runhua Wang received the B.S. degree in automation from Dalian Maritime University, Dalian, China, in 2016, and the Ph.D. degree in control theory and control engineering from Nankai University, Tianjin, China, in 2021. She is currently an Assistant Professor with the Institute of Robotics and Automatic Information System, Nankai University. Her research interests include motion planning and the visual servoing of mobile robots.(Based on document published on 19 June 2023).

Publications

17

Citations

123

Publications by Year

20192024

Co-Authors:

Qingchen Bi
Yongchun Fang
Haiming Gao
Baoquan Li
Chenping Li

Show All Co-Authors (24)

Author's Published Works

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Showing 1-5 of 5 results

Conferences (4)

Journals (1)

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Qingsen Yan(5)
Yu Zhu(5)
Jinqiu Sun(5)
Yanning Zhang(5)
Song Zhang(2)
Northwestern Polytechnical University(3)
Xidian University(2)
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Northwestern Polytechnical University, Xi’an, China(1)
CVL, ETH Zurich(1)
Xi’an University of Architecture and Technology(1)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(1)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE Transactions on Broadcasting(1)
IEEE(5)
Vancouver, BC, Canada(2)
Seattle, WA, USA(1)
Seoul, Korea, Republic of(1)
Convolutional Layers(3)
Diffusion Model(3)
Adam Optimizer(2)
Alignment-based Methods(2)
Deep Neural Network(2)

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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)

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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 |

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)

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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 |

Diffevent: Event Residual Diffusion for Image Deblurring

Pei Wang;Jiumei He;Qingsen Yan;Yu Zhu;Jinqiu Sun;Yanning Zhang

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

Year: 2024 | Conference Paper |

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Traditional frame-based cameras inevitably suffer from non-uniform blur in real-world scenarios. Event cameras that record the intensity changes with high temporal resolution provide an effective solution for image deblurring. In this paper, we formulate the event-based image deblurring as an image generation problem by designing diffusion priors for the image and residual. Specifically, we propos...Show More

Diffevent: Event Residual Diffusion for Image Deblurring

Pei Wang;Jiumei He;Qingsen Yan;Yu Zhu;Jinqiu Sun;Yanning Zhang

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

Year: 2024 | Conference Paper |

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)

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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)

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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 |

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