Loading [a11y]/accessibility-menu.js

Sang Woo Kim - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

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

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Author details

Sang Woo Kim

Also published under: Sang-Woo Kim

Publications

36

Citations

580

Publications by Year

19922016

Co-Authors:

ByungKoo ParkChan-Ho ParkChang-Woo LeeChangWoo LeeCheol-Ho Jeong

Show All Co-Authors (68)

Sang Woo Kim

Also published under: Sang-Woo Kim

Affiliation

Department of Electrical Engineering

POSTECH

Pohang, Korea

Publication Topics

Fault Diagnosis,
Finite Element Analysis,
Least-squares,
Permanent Magnet Synchronous Machines,
Permanent Magnet Synchronous Motor,
Adaptive Algorithm,
Adaptive Filter,
Adaptive Filtering Algorithm,
Additive Noise,
Battery Model,
Circuit Fault,
Computational Analysis

Biography

Sang-Woo Kim received his M.S. degree in Computer Science & Engineering from Hanyang University, Ansan, Korea in 2008. His current research interests include mobile sensor network and mobility management.(Based on document published on 16 September 2010).

Publications

36

Citations

580

Publications by Year

19922016

Co-Authors:

ByungKoo Park
Chan-Ho Park
Chang-Woo Lee
ChangWoo Lee
Cheol-Ho Jeong

Show All Co-Authors (68)

Author's Published Works

Search History

Showing 1-5 of 5 results

Conferences (4)

Journals (1)

Sort

Filter Results

Show

Open Access Only

Range
Single Year
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)

Select All on Page

Sort By

Results

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 |

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 |

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 |

HTML

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)

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 |

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.