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Kai Zhang (张 凯) |
I am looking for self-motivated undergraduate and graduate (Master/Ph.D.) students to join our group. Please send me your CV if you have interest.
课题组现面向南京大学大一至大三的本科生，招募对以下研究方向感兴趣的实习生：
1) 数学算法启发的深度网络结构设计；
2) AI智能相机成像技术（AI+ISP）；
3) 基于预训练模型先验的图像复原；
4) 多曝光图像融合与暗光增强；
5) 图像与视频上色与色彩增强；
6) 其他图像视频复原与增强相关方向。
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
- (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
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MoVideo: Motion-Aware Video Generation with Diffusion Models |
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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis |
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Recurrent Video Restoration Transformer with Guided Deformable Attention |
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Towards Interpretable Video Super-Resolution via Alternating Optimization |
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Plug-and-Play Image Restoration with Deep Denoiser Prior |
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Deep plug-and-play and deep unfolding methods for image restoration (Book chapter) |
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SwinIR: Image Restoration Using Swin Transformer |
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Designing a Practical Degradation Model for Deep Blind Image Super-Resolution |
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Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling |
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Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution |
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Towards Flexible Blind JPEG Artifacts Removal |
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Flow-based Kernel Prior with Application to Blind Super-Resolution |
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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results |
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Deep Unfolding Network for Image Super-Resolution |
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Neural Blind Deconvolution Using Deep Priors |
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NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results |
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AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results |
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Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels |
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Learning a Single Convolutional Super-Resolution Network for Multiple Degradations |
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Learning Deep CNN Denoiser Prior for Image Restoration |
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FFDNet: Toward a Fast and Flexible Solution for CNN-based Image Denoising |
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising |
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End-to-End Blind Image Quality Assessment Using Deep Neural Networks |
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Toward Convolutional Blind Denoising of Real Photographs |
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Convolutional Neural Networks for Image Denoising and Restoration (Book chapter) |
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Joint Learning of Multiple Regressors for Single Image Super-Resolution |
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Revisiting Single Image Super-Resolution Under Internet Environment: Blur Kernels and Reconstruction Algorithms |