  Deep convolution neural networks (CNNs) play a critical role in single image
super-resolution (SISR) since the amazing improvement of high performance
computing. However, most of the super-resolution (SR) methods only focus on
recovering bicubic degradation. Reconstructing high-resolution (HR) images from
randomly blurred and noisy low-resolution (LR) images is still a challenging
problem. In this paper, we propose a novel Spatial Context Hallucination
Network (SCHN) for blind super-resolution without knowing the degradation
kernel. We find that when the blur kernel is unknown, separate deblurring and
super-resolution could limit the performance because of the accumulation of
error. Thus, we integrate denoising, deblurring and super-resolution within one
framework to avoid such a problem. We train our model on two high quality
datasets, DIV2K and Flickr2K. Our method performs better than state-of-the-art
methods when input images are corrupted with random blur and noise.
