Kai Zhang

Kai Zhang (张凯)

Computer Vision Lab
Department of Information Technology and Electrical Engineering, ETH Zurich

ETF D117, Sternwartstrasse 7, ETH Zentrum 8092, Zurich, Switzerland

Email: cskaizhang@gmail.com
[Google Scholar] [Github] [ResearchGate]

Biography

I am currently a postdoctoral researcher at Computer Vision Lab, ETH Zurich, Switzerland, working with Prof. Luc Van Gool and Prof. Radu Timofte. Before that, I received my Ph.D. degree from School of Computer Science and Technology, Harbin Institute of Technology, China, in 2019, under the supervision of Prof. Lei Zhang and Prof. Wangmeng Zuo. I was a research assistant from July, 2015 to July, 2017 and from July, 2018 to April, 2019 in Department of Computing of The Hong Kong Polytechnic University.

Research Interest

I work in the field of image processing, specializing in particular on developing deep learning techniques for inverse problems in low-level computer vision. I mainly investigate how to incorporate traditional model-based method and deep learning-based method for flexible, effective, efficient and interpretable image restoration. Recently, I focus on the following research topics:

Deep plug-and-play image restoration
Deep unfolding image restoration
Camera pipeline
Blind image restoration

PyTorch Toolbox for Image Restoration

KAIR (support training and testing for DnCNN, FFDNet, SRMD, USRNet, ESRGAN)
DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior)

News

(2022-07) I will serve as a Senior Program Committee (SPC) Member for AAAI 2023.
I am organizing a Special Issue "Image Denoising and Image Super-Resolution for Sensing Application" on Sensors (IF 3.576), submit your manuscript before 15 December 2022.
We released the testing codes of SCUNet.
We released the training codes of BSRNet and SwinIR.
All the four submitted papers are accepted by ICCV 2021. Congratulations to Jingyun Liang and Jiaxi Jiang.
One paper is accepted by IEEE TPAMI.
Two papers are accepted in CVPR 2021.
The code of BSRNet is available.
The code of DPIR is available.
The code of USRNet is available.
I am co-organizing the ECCV 2020 Workshop on Advances in Image Manipulation
Two papers are accepted in CVPR 2020.
I am co-organizing the CVPR 2020 Workshop on New Trends in Image Restoration and Enhancement

Selected Publications

	Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Tao Tang, Radu Timofte, Luc Van Gool ArXiv, 2022. [Paper] [PyTorch Testing Code] [Online demo] [BibTex]
	Recurrent Video Restoration Transformer with Guided Deformable Attention Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc Van Gool Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS*), 2022. [Paper] [PyTorch Code] [BibTex]
	Towards Interpretable Video Super-Resolution via Alternating Optimization Jiezhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang, Qin Wang, Yulun Zhang, Hao Tang, Luc Van Gool European Conference on Computer Vision (ECCV*), 2022. [Paper] [PyTorch Code] [BibTex]
	Plug-and-Play Image Restoration with Deep Denoiser Prior Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte IEEE Transactions on Pattern Analysis and Machine Intelligence(IEEE TPAMI), 2022. [Paper] [PyTorch Code] [BibTex]
	Deep plug-and-play and deep unfolding methods for image restoration (Book chapter) Kai Zhang, Radu Timofte In: E.R. Davies and Matthew A. Turk (eds.), Advanced Methods and Deep Learning in Computer Vision, Academic Press, 2022. [Paper] [BibTex]
	SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision Workshops (ICCVW*), 2021. [Paper] [PyTorch Testing Code] [PyTorch Training Code] [BibTex]
	Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Kai Zhang, Jingyun Liang, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision (ICCV), 2021. [Paper] [PyTorch Code] [BibTex]
	Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision (ICCV*), 2021. [Paper] [PyTorch Code] [BibTex]
	Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution Jingyun Liang, Guolei Sun, Kai Zhang, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision (ICCV*), 2021. [Paper] [PyTorch Code] [BibTex]
	Towards Flexible Blind JPEG Artifacts Removal Jiaxi Jiang, Kai Zhang, Radu Timofte IEEE International Conference on Computer Vision (ICCV*), 2021. [Paper] [PyTorch Code] [BibTex]
	Flow-based Kernel Prior with Application to Blind Super-Resolution Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision and Pattern Recognition (CVPR*), 2021. [Paper] [PyTorch Code] [BibTex]
	AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, others European Conference on Computer Vision Workshops (ECCVW), 2020. [Paper] [BibTex]
	Deep Unfolding Network for Image Super-Resolution Kai Zhang, Luc Van Gool, Radu Timofte IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Paper] [PyTorch Code] [BibTex]
	Neural Blind Deconvolution Using Deep Priors Dongwei Ren, Kai Zhang, Qilong Wang, Qinghua Hu, Wangmeng Zuo IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), 2020. [Paper] [PyTorch Code] [BibTex]
	NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results Kai Zhang, Shuhang Gu, Radu Timofte, and others IEEE International Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2020. [Paper] [BibTex]
	AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results Kai Zhang, Shuhang Gu, Radu Timofte, and others IEEE International Conference on Computer Vision Workshops (ICCVW), 2019. [Paper] [PyTorch Code of Winner] [BibTex]
	Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels Kai Zhang, Wangmeng Zuo, Lei Zhang IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [Paper] [PyTorch Code] [BibTex]
	Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [Paper] [Matlab Code] [PyTorch Code] [BibTex] [Citations: 670+]
	Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang, Wangmeng Zuo, Shuhang Gu, Lei Zhang IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [Paper] [Matlab Code] [BibTex] [Citations: 1400+]
	FFDNet: Toward a Fast and Flexible Solution for CNN-based Image Denoising Kai Zhang, Wangmeng Zuo, Lei Zhang IEEE Transactions on Image Processing (TIP), 27(9): 4608-4622, 2018. [Paper] [Matlab Code] [PyTorch Code] [BibTex] [Citations: 1300+]
	Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang IEEE Transactions on Image Processing (TIP), 26(7): 3142-3155, 2017. [Paper] [Matlab Code] [PyTorch Code] [BibTex] [Citations: 4900+]
	End-to-End Blind Image Quality Assessment Using Deep Neural Networks Kede Ma, Wentao Liu, Kai Zhang, Zhengfang Duanmu, Zhou Wang, Wangmeng Zuo IEEE Transactions on Image Processing (TIP), 27(3): 1202-1213, 2017. [Paper] [Project Page] [BibTex] [Citations: 300+]
	Toward Convolutional Blind Denoising of Real Photographs Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, Lei Zhang IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [Paper] [Code] [BibTex] [Citations: 550+]
	Convolutional Neural Networks for Image Denoising and Restoration (Book chapter) Wangmeng Zuo, Kai Zhang, Lei Zhang In: M. Bertalmio (eds.), Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends, Springer, 2018. [Paper] [BibTex]
	Joint Learning of Multiple Regressors for Single Image Super-Resolution Kai Zhang, Baoquan Wang, Wangmeng Zuo, Hongzhi Zhang, Lei Zhang. IEEE Signal Processing Letters (SPL), 23, (1): 102-106, 2016. [Paper] [BibTex] [Citations: 30+]
	Revisiting Single Image Super-Resolution Under Internet Environment: Blur Kernels and Reconstruction Algorithms Kai Zhang, Xiaoyu Zhou, Hongzhi Zhang, Wangmeng Zuo. Pacific Rim Conference on Multimedia (PCM), 2015: 677-687 [Paper] [BibTex]

Services

Workshop Organizers:

Co-organizer of ECCV 2020 Workshop on Advanced Image Manipulation (AIM).
Co-organizer of CVPR 2020 Workshop on New Trends in Image Restoration and Enhancement (NTIRE).
Co-organizer of ICCV 2019 Workshop on Advanced Image Manipulation (AIM).

Journal Reviewer: