Carview!

CARVIEW

MOTORHOMES

Select Language

HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Fri, 15 Aug 2025 03:11:25 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"689ea55d-7d6f" expires: Sun, 28 Dec 2025 21:43:25 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 8B7C:15317B:807D78:902B73:6951A225 accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 21:33:25 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210053-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766957605.406657,VS0,VE218 vary: Accept-Encoding x-fastly-request-id: 365ffbe5e94031e6823138efdf00527486d2bf4c content-length: 8251 Kai Zhang

Kai Zhang (张凯)

School of Intelligence Science and Technology, Nanjing University, Suzhou, China

Email: kaizhang@nju.edu.cn cskaizhang@gmail.com
[Google Scholar] [Github] [ResearchGate] [ORCID] [Semantic Scholar] [DBLP]

课题组长期招收优秀的同学加入：

招收对象

本科生：南京大学及其他高校的大一、大二、大三学生，需保证至少一年的进组科研时间，特别优秀者提供适当科研补助。
研究生：硕士/博士研究生，背景包括计算机、数学等相关专业。
研究人员：全职/兼职研究助理。

关于招收硕士研究生的说明（该内容参考Link）

招收对象是愿意全身心投入做好科研、学习专业知识的学生。
可以接受论文精读理解、代码实现、PPT报告等考核。
对自己的编程能力有自信，并且有一定的数学功底。
如成功保研，愿意在课题组内做大四毕设，提前熟悉相关背景知识，并且在研究生入学之前投稿一篇文章。

关于招收博士研究生的说明

以第一作者身份在CCF A类会议或期刊发表至少一篇论文。
具有生成式大模型相关研究经验者优先考虑。

Biography

I am an Associate Professor of the School of Intelligence Science and Technology at Nanjing University, as of March 2024. Previously, I was a postdoctoral researcher at Computer Vision Lab, ETH Zurich, Switzerland. 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, with a specific focus on developing deep learning techniques for inverse problems in low-level computer vision. I mainly explore incorporating model-based methods and learning-based methods 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

(2025-06) Two papers are accepted in ICCV 2025.
(2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026.
(2024-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2025.
We are organizing a Special Issue titled "Recent Advances in Image Reconstruction" for Visual Intelligence. Submissions of high-quality articles are warmly welcome.
Four papers are accepted in CVPR 2024.
(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

	Reverse Convolution and Its Applications to Image Restoration Xuhong Huang†, Shiqi Liu†, Kai Zhang, Ying Tai, Jian Yang, Hui Zeng, Lei Zhang IEEE International Conference on Computer Vision (ICCV*), 2025. [Paper] [Project page] [BibTex]
	MoVideo: Motion-Aware Video Generation with Diffusion Models Jingyun Liang, Yuchen Fan, Kai Zhang*, Radu Timofte, Luc Van Gool, Rakesh Ranjan European Conference on Computer Vision, 2024. [Paper] [Project page] [BibTex]
	Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis Kai Zhang*, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Tao Tang, Deng-Ping Fan, Radu Timofte, Luc Van Gool Machine Intelligence Research, 2023. [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: