  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.
