Deep Plug-and-Play Image Restoration

Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte

Computer Vision Lab, ETH Zurich, Switzerland

Denoising results on BSD68 and Urban100 datasets

Dataset	Noise Level	FFDNet-PSNR(RGB)	FFDNet-PSNR(Y)	DRUNet-PSNR(RGB)	DRUNet-PSNR(Y)
CBSD68	30	30.32	32.05	30.81	32.44
CBSD68	50	27.97	29.65	28.51	30.09
Urban100	30	30.53	32.72	31.83	33.93
Urban100	50	28.05	30.09	29.61	31.57

PSNR(Y) means the PSNR is calculated on the Y channel of YCbCr space.

Abstract

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 DRUNet Denoiser (state-of-the-art Gaussian denoising performance!)

Network architecture
Grayscale image denoising
Color image denoising


(a) Noisy image with noise level 200	(b) Result by the proposed DRUNet denoiser

Even trained on noise level range of [0, 50], DRUNet can still perform well on an extremely large unseen noise level of 200.

Image Deblurring

Visual comparison

Single Image Super-Resolution

Visual comparison

Color Image Demosaicing

PSNR
Visual comparison

Citation

@article{zhang2021plug,
  title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
  author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={10},
  pages={6360-6376},
  year={2021}
}
 @inproceedings{zhang2017learning,
   title={Learning Deep CNN Denoiser Prior for Image Restoration},
   author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},
   booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
   pages={3929--3938},
   year={2017},
 }

lucciffer/DPIR