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-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.
Abstract (translated)
基于深度神经网络（DNN）的单图像超分辨率（SISR）方法在迅速普及的同时，主要是针对广泛使用的双三次退化而设计的，对于它们而言，用任意模糊核超分辨率（LR）图像仍然存在着根本的挑战。同时，即插即用图像恢复由于其模块化结构，易于实现去噪先验的插件化，被公认为具有很高的灵活性。本文提出了一种利用即插即用框架扩展基于双三次退化的深部SISR来处理任意模糊核的LR图像的原理公式和框架。具体地，我们设计了一个新的SISR退化模型，以利用现有的盲去模糊方法进行模糊核估计。为了优化新的降质诱导能量函数，我们通过变量分割技术推导了一种即插即用算法，使得我们可以将任何超级分解器的前级作为模块化部分而不是去噪前级。对合成和真实的LR图像进行定量和定性评估表明，所提出的深即插即用超分辨率框架能够灵活有效地处理模糊的LR图像。
URL
https://arxiv.org/abs/1903.12529
