Blind Image Super-Resolution with Spatial Context Hallucination

• URL: http://arxiv.org/abs/2009.12461v1
• Date: Fri, 25 Sep 2020 22:36:07 GMT
• Title: Blind Image Super-Resolution with Spatial Context Hallucination
• Authors: Dong Huo, Yee-Hong Yang
• Abstract summary: We propose a novel Spatial Context Hallucination Network (SCHN) for blind super-resolution without knowing the degradation kernel. 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.
• Score: 5.849485167287474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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