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Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang A, Luc Van Gool, Radu Timofte
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR.
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Zhang, K., Li, Y., Zuo, W., A, L. Z., Gool, L. V., & Timofte, R. (2021). Plug-and-Play Image Restoration With Deep Denoiser Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6360–6376. https://doi.org/10.1109/tpami.2021.3088914
Zhang, Kai, et al. "Plug-and-Play Image Restoration With Deep Denoiser Prior." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, 2021, pp. 6360–6376, https://doi.org/10.1109/tpami.2021.3088914.
Zhang, Kai, Yawei Li, Wangmeng Zuo, Lei Zhang A, Luc Van Gool, and Radu Timofte. "Plug-and-Play Image Restoration With Deep Denoiser Prior." IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 10 (2021): 6360–6376. https://doi.org/10.1109/tpami.2021.3088914.
Zhang K., Li Y., Zuo W. [и др.] Plug-and-Play Image Restoration With Deep Denoiser Prior // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 2021. — Vol. 44, № 10. — P. 6360–6376. — URL: https://doi.org/10.1109/tpami.2021.3088914
@article{zhang2021plugandplay,
title = {{Plug-and-Play Image Restoration With Deep Denoiser Prior}},
author = {Kai Zhang and Yawei Li and Wangmeng Zuo and Lei Zhang A and Luc Van Gool and Radu Timofte},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2021},
volume = {44},
number = {10},
pages = {6360--6376},
doi = {10.1109/tpami.2021.3088914},
url = {https://doi.org/10.1109/tpami.2021.3088914}
}TY - JOUR AU - Zhang, Kai AU - Li, Yawei AU - Zuo, Wangmeng AU - A, Lei Zhang AU - Gool, Luc Van AU - Timofte, Radu TI - Plug-and-Play Image Restoration With Deep Denoiser Prior JO - IEEE Transactions on Pattern Analysis and Machine Intelligence VL - 44 IS - 10 SP - 6360 EP - 6376 PY - 2021 DA - 2021/06/14 DO - 10.1109/tpami.2021.3088914 UR - https://doi.org/10.1109/tpami.2021.3088914 ER -
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