  While deep neural networks (DNN) based single image super-resolution (SISR)
methods are rapidly gaining popularity, they are mainly designed for the
widely-used bicubic degradation, and there still remains the fundamental
challenge for them to super-resolve low-resolution (LR) image with arbitrary
blur kernels. In the meanwhile, plug-and-play image restoration has been
recognized with high flexibility due to its modular structure for easy plug-in
of denoiser priors. In this paper, we propose a principled formulation and
framework by extending bicubic degradation based deep SISR with the help of
plug-and-play framework to handle LR images with arbitrary blur kernels.
Specifically, we design a new SISR degradation model so as to take advantage of
existing blind deblurring methods for blur kernel estimation. To optimize the
new degradation induced energy function, we then derive a plug-and-play
algorithm via variable splitting technique, which allows us to plug any
super-resolver prior rather than the denoiser prior as a modular part.
Quantitative and qualitative evaluations on synthetic and real LR images
demonstrate that the proposed deep plug-and-play super-resolution framework is
flexible and effective to deal with blurry LR images.
