Kangfu MEI

I am a M.Phil student at The Chinese University of Hong Kong, Shenzhen, where I work on compute vision and computational photography, advised by Prof. Rui Huang.

I'm currently working at DAMO Academy, Alibaba-Group as a research intern.

I previously interned at: Kuaishou  /  JD.COM

Email  /  CV  /  Google Scholar  /  Github  /  Zhihu

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Research

My research interest mainly focuses on degraded images restoration as well as its applicatoin in high-level vision. Representative papers are highlighted.

Disentangle Perceptual Learning through Online Contrastive Learning
Kangfu Mei, Yao Lu, Qiaosi Yi, Haoyu Wu, Juncheng Li, Rui Huang
arXiv, 2020
Work in Progress / project page / arXiv

Enhance the realism of resotred images using a simple contrastive learning module that disentangles perception-relevant dimensions in self-supervised manner.

Spatial Pixel Irrelevant Network for Learning Image-to-image Translation
Kangfu Mei, Haoyu Wu, Juncheng Li, Rui Huang
arXiv, 2020
Work in Progress

Enable networks to learn the image translation without aligned pairs and achieve the state-of-the-art results in the real-world super-resolution task.

Visual and Semantic Scene Understanding Under Bad Weather
Kangfu Mei, Haoyu Wu, Juncheng Li, Rui Huang
arXiv, 2019
Work in Progress

Address the semantic segmentation accuracy dropping problem in dehazed images using the proposed semantic guiding loss.

Higher-resolution Network for Image Demosaicing and Enhancing
Kangfu Mei, Juncheng Li, Jiajie Zhang, Haoyu Wu, Jie Li, Rui Huang
ICCV Worshop, 2019   (AIM2019 Winner Award)
code / bibtex

Achieved the best visual quality in Advances in Image Manipulation Workshop (AIM2019) Challenge on RAW to RGB Mapping.

Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution
Juncheng Li, Yiting Yuan, Kangfu Mei, Faming Fang
ICCV Worshop, 2019   (Oral Presentation)
code / bibtex

A lightweight Super-resolution framework that can construct infinitely possible topological sub-structure through Fractal Tree.

Progressive Feature Fusion Network for Realistic Image Dehazing
Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang
ACCV, 2018
code / bibtex

The first proposed haze removal method that removes haze from a single image in the complete end-to-end manner.

Deep Residual Refining based Pseudo-multi-frame Network for Effective Single Image Super-resolution
Kangfu Mei, Aiwen Jiang, Juncheng Li, Bo Liu, Jihua Ye, Mingwen Wang
IET Image Processing, 2018
code / bibtex

The first proposed haze removal method that removes haze from a single image in the complete end-to-end manner.

Multi-scale Residual Network for Image Super-resolution
Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang
ECCV, 2018
code / bibtex

Introduce a novel multi-scale residual network for recovering the high-quality image from low-resolution.

Effective Single-image Super-resolution Model using Squeeze-and-excitation Networks
Kangfu Mei, Aiwen Jiang, Juncheng Li, Jihua Ye, Mingwen Wang
ICNOIP, 2018
arXiv / code / bibtex

Using squeeze-and-excitation blocks to improve the visual quality of super-resolution results with finer texture details.

Service

Reviewer of International Journal of Computer Vision (IJCV)

Reviewer of IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

Reviewer of Computer Vision and Image Understanding (CVIU)

Prizes

AIM2019 Mobile Raw to DSLR RGB Image Mapping Challenge (ICCV2019 Workshop): Top 1

Alibaba Youku Video Enhancement and Super-Resolution Challenge 2019: Top 4

NTIRE2018 Image Dehazing Challenge (CVPR2018 Workshop): Honorable Mention Award & Top 6

University Computer Software Programming Challenge 2018 in The Pearl River Delta: Gold Award & Best innovative Award