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Kangfu Mei - IEEE Xplore Author Profile
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Kangfu Mei

Publications
11
Citations
253
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (109)
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Kangfu Mei

Affiliation

Google Research
Johns Hopkins University

Publication Topics

Biography

Kangfu Mei received the B.Sc. degree in computer science and technology from Jiangxi Normal University, Nanchang, China, in 2019. He is currently pursuing the M.Phil. degree with the School of Science and Engineering, The Chinese University of Hong Kong at Shenzhen, Shenzhen, China. His main research interests include image generation, image processing, and computational photography.(Based on document published on 29 September 2020).
Publications
11
Citations
253
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (109)
Author's Published Works

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CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation

Kangfu Mei;Mauricio Delbracio;Hossein Talebi;Zhengzhong Tu;Vishal M. Patel;Peyman Milanfar

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Large generative diffusion models have revolution-ized text-to-image generation and offer immense po-tential for conditional generation tasks such as im-age enhancement, restoration, editing, and compositing. However, their widespread adoption is hindered by the high computational cost, which limits their real-time application. To address this challenge, we in-troduce a novel method dubbed CoDi, t...Show More

CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation

Kangfu Mei;Mauricio Delbracio;Hossein Talebi;Zhengzhong Tu;Vishal M. Patel;Peyman Milanfar

Year: 2024 | Conference Paper |

Latent Feature-Guided Diffusion Models for Shadow Removal

Kangfu Mei;Luis Figueroa;Zhe Lin;Zhihong Ding;Scott Cohen;Vishal M. Patel

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images. In this paper, we propose the use of diffusion models as they offer a promising approach to gradually refine the details of shadow regions during the diffusion process. Our method improves this process by conditioning on a learned latent feature space that ...Show More

Latent Feature-Guided Diffusion Models for Shadow Removal

Kangfu Mei;Luis Figueroa;Zhe Lin;Zhihong Ding;Scott Cohen;Vishal M. Patel

Year: 2024 | Conference Paper |

LTT-GAN: Looking Through Turbulence by Inverting GANs

Kangfu Mei;Vishal M. Patel

IEEE Journal of Selected Topics in Signal Processing
Year: 2023 | Volume: 17, Issue: 3 | Journal Article |
Cited by: Papers (12)
In many applications of long-range imaging, such as surveillance, we are faced with a scenario where we have to recover and identify facial images appearing in the captured imagery degraded by atmospheric turbulence. One way to deal with this problem is to develop methods that can remove the effect of turbulence from images. However, restoring images degraded by atmospheric turbulence is difficult...Show More

LTT-GAN: Looking Through Turbulence by Inverting GANs

Kangfu Mei;Vishal M. Patel

Year: 2023 | Volume: 17, Issue: 3 | Journal Article |

A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration

Nithin Gopalakrishnan Nair;Kangfu Mei;Vishal M. Patel

2022 IEEE International Conference on Image Processing (ICIP)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to a drastic drop in performance when computer vision algorithms like object/face recognition and detection are performed on these images. In re-cent years, various deep learning-based atmospheric turbulence mitigation meth...Show More

A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration

Nithin Gopalakrishnan Nair;Kangfu Mei;Vishal M. Patel

Year: 2022 | Conference Paper |

Thermal to Visible Image Synthesis Under Atmospheric Turbulence

Kangfu Mei;Yiqun Mei;Vishal M. Patel

2022 IEEE International Conference on Image Processing (ICIP)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions. However, such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images. Such an issue is unavoidable in long-range imaging and sig...Show More

Thermal to Visible Image Synthesis Under Atmospheric Turbulence

Kangfu Mei;Yiqun Mei;Vishal M. Patel

Year: 2022 | Conference Paper |

SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom

Kangfu Mei;Shenglong Ye;Rui Huang

2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts when dealing with real-world super-resolution problems due to the difficulty in learning misaligned optical zoom. In this paper, we introduce a Squared Deformable Alignment Network (SDAN) to address this issue. Our networ...Show More

SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom

Kangfu Mei;Shenglong Ye;Rui Huang

Year: 2021 | Conference Paper |

MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution

Juncheng Li;Faming Fang;Jiaqian Li;Kangfu Mei;Guixu Zhang

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2021 | Volume: 31, Issue: 7 | Journal Article |
Cited by: Papers (67)
Convolutional neural networks have been proven to be of great benefit for single-image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are committed to mining image features and le...Show More

MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution

Juncheng Li;Faming Fang;Jiaqian Li;Kangfu Mei;Guixu Zhang

Year: 2021 | Volume: 31, Issue: 7 | Journal Article |

HighEr-Resolution Network for Image Demosaicing and Enhancing

Kangfu Mei;Juncheng Li;Jiajie Zhang;Haoyu Wu;Jie Li;Rui Huang

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (22)
Neural-networks based image restoration methods tend to use low-resolution image patches for training. Although higher-resolution image patches can provide more global information, state-of-the-art methods cannot utilize them due to their huge GPU memory usage, as well as the instable training process. However, plenty of studies have shown that global information is crucial for image restoration t...Show More

HighEr-Resolution Network for Image Demosaicing and Enhancing

Kangfu Mei;Juncheng Li;Jiajie Zhang;Haoyu Wu;Jie Li;Rui Huang

Year: 2019 | Conference Paper |

AIM 2019 Challenge on RAW to RGB Mapping: Methods and Results

Andrey Ignatov;Radu Timofte;Sung-Jea Ko;Seung-Wook Kim;Kwang-Hyun Uhm;Seo-Won Ji;Sung-Jin Cho;Jun-Pyo Hong;Kangfu Mei;Juncheng Li;Jiajie Zhang;Haoyu Wu;Jie Li;Rui Huang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Yuzhi Zhao;Lai-Man Po;Tiantian Zhang;Zongbang Liao;Xiang Shi;Yujia Zhang;Weifeng Ou;Pengfei Xian;Jingjing Xiong;Chang Zhou;Wing Yin Yu;Yubin Yubin;Bingxin Hou;Bumjun Park;Songhyun Yu;Sangmin Kim;Jechang Jeong

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (33)
This paper reviews the first AIM challenge on mapping camera RAW to RGB images with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map the original low-quality RAW images from the Huawei P20 device to the same photos captured with the Canon 5D DSLR camera. The considered problem embraced a number of co...Show More

AIM 2019 Challenge on RAW to RGB Mapping: Methods and Results

Andrey Ignatov;Radu Timofte;Sung-Jea Ko;Seung-Wook Kim;Kwang-Hyun Uhm;Seo-Won Ji;Sung-Jin Cho;Jun-Pyo Hong;Kangfu Mei;Juncheng Li;Jiajie Zhang;Haoyu Wu;Jie Li;Rui Huang;Muhammad Haris;Greg Shakhnarovich;Norimichi Ukita;Yuzhi Zhao;Lai-Man Po;Tiantian Zhang;Zongbang Liao;Xiang Shi;Yujia Zhang;Weifeng Ou;Pengfei Xian;Jingjing Xiong;Chang Zhou;Wing Yin Yu;Yubin Yubin;Bingxin Hou;Bumjun Park;Songhyun Yu;Sangmin Kim;Jechang Jeong

Year: 2019 | Conference Paper |

Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution

Juncheng Li;Yiting Yuan;Kangfu Mei;Faming Fang

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (22)
Convolutional neural networks have recently achieved great success in image super-resolution (SR). However, we notice an interesting phenomenon that these SR models are getting bigger, deeper, and more complex. Extensive models promote the development of SR, but the effectiveness, reproducibility and practical application prospects of these new models need further verification. In this paper, we p...Show More

Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution

Juncheng Li;Yiting Yuan;Kangfu Mei;Faming Fang

Year: 2019 | Conference Paper |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

Year: 2018 | Conference Paper |

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