Degradation-Guided Meta-Restoration Network for Blind Super-Resolution

• URL: http://arxiv.org/abs/2207.00943v1
• Date: Sun, 3 Jul 2022 03:24:45 GMT
• Title: Degradation-Guided Meta-Restoration Network for Blind Super-Resolution
• Authors: Fuzhi Yang, Huan Yang, Yanhong Zeng, Jianlong Fu, Hongtao Lu
• Abstract summary: Blind super-resolution (SR) aims to recover high-quality visual textures from a low-resolution (LR) image. Existing SR approaches either assume a predefined blur kernel or a fixed noise, which limits these approaches in challenging cases. We propose a Degradation-guided Meta-restoration network for blind Super-Resolution (DMSR) that facilitates image restoration for real cases.
• Score: 45.61951760826198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Blind super-resolution (SR) aims to recover high-quality visual textures from a low-resolution (LR) image, which is usually degraded by down-sampling blur kernels and additive noises. This task is extremely difficult due to the challenges of complicated image degradations in the real-world. Existing SR approaches either assume a predefined blur kernel or a fixed noise, which limits these approaches in challenging cases. In this paper, we propose a Degradation-guided Meta-restoration network for blind Super-Resolution (DMSR) that facilitates image restoration for real cases. DMSR consists of a degradation extractor and meta-restoration modules. The extractor estimates the degradations in LR inputs and guides the meta-restoration modules to predict restoration parameters for different degradations on-the-fly. DMSR is jointly optimized by a novel degradation consistency loss and reconstruction losses. Through such an optimization, DMSR outperforms SOTA by a large margin on three widely-used benchmarks. A user study including 16 subjects further validates the superiority of DMSR in real-world blind SR tasks.

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