Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels [article]

Kai Zhang, Wangmeng Zuo, Lei Zhang
<span title="2019-03-29">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... rincipled 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.12529v1">arXiv:1903.12529v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3wgb7l3l7nam5pn3rk4n7zonye">fatcat:3wgb7l3l7nam5pn3rk4n7zonye</a> </span>
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