Abstract
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-andplay 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.