Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

Kai Zhang; Yawei Li; Jingyun Liang; Jiezhang Cao; Yulun Zhang; Hao Tang; Deng-Ping Fan; Radu Timofte; Luc Van Gool

doi:10.1007/s11633-023-1466-0

Volume 20 Issue 6

December 2023

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Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc Van Gool. Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis. Machine Intelligence Research, vol. 20, no. 6, pp.822-836, 2023. https://doi.org/10.1007/s11633-023-1466-0

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Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc Van Gool. Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis. Machine Intelligence Research, vol. 20, no. 6, pp.822-836, 2023. https://doi.org/10.1007/s11633-023-1466-0

Citation:

Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc Van Gool. Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis. Machine Intelligence Research, vol. 20, no. 6, pp.822-836, 2023. https://doi.org/10.1007/s11633-023-1466-0

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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

doi: 10.1007/s11633-023-1466-0

Kai Zhang^{1

,},
Yawei Li^{1

,},
Jingyun Liang^{1

,},
Jiezhang Cao^{1

,},
Yulun Zhang^{1

,},
Hao Tang^{1

,},
Deng-Ping Fan^{1

,},
Radu Timofte^{2

,},
Luc Van Gool^{1, 3

,}

1.
Computer Vision Lab, ETH Zürich, Zürich 8092, Switzerland
2.
Computer Vision Lab, University of Würzburg, Würzburg 97074, Germany
3.
KU Leuven, Leuven 3000, Belgium

More Information

Author Bio:
Kai Zhang received the Ph. D. degree in computer science from School of Computer Science and Technology, Harbin Institute of Technology, China in 2019. He is currently a postdoctoral researcher at Computer Vision Lab, ETH Zürich, Switzerland. He was a research assistant from July, 2015 to July, 2017 and from July, 2018 to April, 2019 in the Department of Computing, The Hong Kong Polytechnic University, China. He has proposed several popular image restoration methods, such as DnCNN, SRMD, USRNet, DPIR, SwinIR, and BSRGAN, which have received more than 10000 citations. His research interest mainly focuses on developing flexible, effective, efficient, and interpretable deep learning techniques for inverse problems in low-level computer vision.E-mail: cskaizhang@gmail.com (Corresponding author)ORCID iD: 0000-0002-6319-3722

Yawei Li received the B. Eng. and B. Econ. degrees in computer science in 2014 and the M. Eng. degree in computer science in 2017 both from the University of Electronic Science and Technology of China. He is a Ph. D. degree candidate at Computer Vision Laboratory, ETH Zürich, Switzerland, supervised by Prof. Luc Van Gool and Prof. Radu Timofte. His research interests include image restoration and enhancement, model acceleration, and network compression.E-mail: yawei.li@vision.ee.ethz.chORCID iD: 0000-0002-8948-7892

Jingyun Liang received the B. Sc. degree in information system engineering from the National University of Defense Technology, China in 2016, where he also received the M. Sc. degree in control science and engineering in 2019. He is a Ph.D. degree candidate at the Computer Vision Lab, Department of Information Technology and Electrical Engineering, ETH Zürich, Switzeland since September 2019. His research interest is low-level vision, especially image and video restoration, such as image/video super-resolution, deblurring and denoising.E-mail: jinliang@vison.ee.ethz.ch

Jiezhang Cao received the B. Sc. degree in statistics from Guangdong University of Technology, China in 2017, and the M. Eng. degree in School of Software Engineering, South China University of Technology, China in 2020. He is a Ph. D. degree candidate at Department of Information Technology and Electrical Engineering, ETH Zürich, Switzerland since November 2020. His research interests include machine learning and image/video restoration.E-mail: jiezhang.cao@vision.ee.ethz.chORCID iD: 0000-0002-0050-4714

Yulun Zhang received the B. Eng. degree in intelligence science and technology from the School of Electronic Engineering, Xidian University, China in 2013, and the M. Eng. degree in control engineering from the Department of Automation, Tsinghua University, China in 2017. He received the Ph. D. degree in computer engineering from Department of ECE, Northeastern University, USA in 2021. He is a postdoctoral researcher at Computer Vision Lab, ETH Zürich, Switzerland. He also worked as a research fellow in Harvard University, USA. He was the recipitents of the Best Student Paper Award at VCIP in 2015 and the Best Paper Award at ICCV RLQ Workshop in 2019. His research interests include image/video restoration and synthesis, biomedical image analysis, model compression, and computational imaging.E-mail: yulun100@gmail.comORCID iD: 0000-0002-2288-5079

Hao Tang received the M. Eng. degree in computer science from School of Electronics and Computer Engineering, Peking University, China in 2016, and the Ph. D. degree in computer science from the Multimedia and Human Understanding Group, University of Trento, Italy in 2021. He is currently a postdoctoral with Computer Vision Lab, ETH Zürich, Switzerland. He was a visiting scholar in Department of Engineering Science, University of Oxford, UK. His research interests include deep learning, machine learning, and their applications to computer vision.E-mail: hao.tang@vision.ee.ethz.ch

Deng-Ping Fan received the Ph. D. degree from Nankai University, China in 2019. He joined the Inception Institute of Artificial Intelligence (IIAI), UAE in 2019. He has published approximately 50 top journal and conference papers such as TPAMI, CVPR, ICCV, ECCV, etc. He won the Best Paper Finalist Award at IEEE CVPR 2019, and the Best Paper Award Nominee at IEEE CVPR 2020. He was recognized as the CVPR 2019 outstanding reviewer with a special mention award, the CVPR 2020 outstanding reviewer, the ECCV 2020 high-quality reviewer, and the CVPR 2021 outstanding reviewer. He served as a program committee board (PCB) member of IJCAI 2022–2024, a senior program committee (SPC) member of IJCAI 2021, a committee member of China Society of Image and Graphics (CSIG), area chair in NeurIPS 2021 Datasets and Benchmarks Track, area chair in MICCAI2020 Workshop (OMIA7), Associate Editor of Machine Intelligence Research.His research interests include computer vision, deep learning, and visual attention, especially the human vision on co-salient object detection, RGB salient object detection, RGB-D salient object detection, and video salient object detection.E-mail: dengpfan@gmail.comORCID iD: 0000-0002-5245-7518

Radu Timofte received the Ph. D. degree in electrical engineering from the KU Leuven, Belgium in 2013. From 2013 to 2016, he was postdoc in the Computer Vision Lab, ETH Zürich, Switzerland. He is currently group leader and lecturer in the same lab. He is an editorial board member of top journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Elsevier Neurocomputing, Elsevier Computer Vision and Image Understanding, SIAM Journal on Imaging Sciences and served(s) as an area chair for top conferences such as CVPR 2021, IJCAI 2021, ECCV 2020, ACCV 2020, ICCV 2019. His work received several awards. He is a co-founder of Merantix and a co-organizer of NTIRE, CLIC, AIM, and PIRM events. His research interests include deep learning, implicit models, compression, tracking, restoration and enhancement.E-mail: radu.timofte@uni-wuerzburg.deORCID iD: 0000-0002-1478-0402

Luc Van Gool received the B. Eng. degree in electromechanical engineering from the Katholieke Universiteit Leuven in 1981. Currently, he is a professor at the Katholieke Universiteit Leuven in Belgium and the ETH Zürich, Switzerland. He leads computer vision research at both places, and also teaches at both. He has been a program committee member of several major computer vision conferences. He received several Best Paper awards, won a David Marr Prize and a Koenderink Award, and was nominated Distinguished Researcher by the IEEE Computer Science committee. He is a co-founder of 10 spin-off companies.His research interests include 3D reconstruction and modelling, object recognition, tracking, and gesture analysis, and the combination of those.E-mail: vangool@vision.ee.ethz.chORCID iD: 0000-0002-3445-5711
Received Date: 2022-11-18
Accepted Date: 2023-08-07
Publish Online: 2023-09-15
Publish Date: 2023-12-01

Abstract

Abstract

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research. The source code is available at https://github.com/cszn/SCUNet.
- Blind image denoising,
- real image denosing data synthesis,
- Transformer,
- image signal processing (ISP) pipeline

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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

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