论文标题
盲目超分辨率的降解引导的元恢复网络
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution
论文作者
论文摘要
盲级超分辨率（SR）旨在从低分辨率（LR）图像中恢复高质量的视觉纹理，通常通过下采样模糊内核和添加噪声来降低。由于现实世界中复杂的图像降解的挑战，此任务非常困难。现有的SR方法假定预定义的模糊内核或固定噪声，这限制了这些方法在具有挑战性的情况下。在本文中，我们提出了一个用于盲人超分辨率（DMSR）的降解引导的元估计网络，该网络促进了真实情况的图像恢复。 DMSR由降解提取器和元修复模块组成。提取器估计了LR输入中的降解，并指导元恢复模块以预测恢复参数的恢复参数。 DMSR通过新颖的降解一致性损失和重建损失共同优化。通过这样的优化，DMSR在三个广泛使用的基准上的优于SOTA的优于SOTA。一项包括16个受试者的用户研究进一步验证了在现实世界中的盲目SR任务中DMSR的优势。
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.
