Single image super-resolution (SISR) is an image processing task which obtains high-resolution (HR) image from a low-resolution (LR) image. Recently, due to the capability in feature extraction, a series of deep learning methods have brought important crucial improvement for SISR. However, we observe that no matter how deeper the networks are designed, they usually do not have good generalization ability, which leads to the fact that almost all of existing SR methods have poor performances on restoration of the weak texture details. To solve these problems, we propose a weak texture information map guided image super-resolution with deep residual networks. It contains three sub-networks, one main network which extracts the main features and fuses weak texture details, another two auxiliary networks extract the weak texture details fallen in the main network. Two part of networks work cooperatively, the auxiliary networks predict and integrates week texture information into the main network, which is conducive to the main network learning more inconspicuous details. Experiments results demonstrate that our method's performs achieve the state-of-the-art quantitatively. Specifically, the image super-resolution results of our method own more weak texture details.

中文翻译：

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具有深度残差网络的弱纹理信息图引导图像超分辨率

单幅图像超分辨率 (SISR) 是一种图像处理任务，它从低分辨率 (LR) 图像中获取高分辨率 (HR) 图像。最近，由于在特征提取方面的能力，一系列深度学习方法为 SISR 带来了重要的关键改进。然而，我们观察到，无论网络设计得有多深，它们通常都没有很好的泛化能力，这导致几乎所有现有的 SR 方法在弱纹理细节的恢复上都表现不佳。为了解决这些问题，我们提出了一种具有深度残差网络的弱纹理信息图引导图像超分辨率。它包含三个子网络，一个主网络提取主要特征并融合弱纹理细节，另外两个辅助网络提取落在主网络中的弱纹理细节。两部分网络协同工作，辅助网络预测周纹理信息并将其整合到主网络中，有利于主网络学习更多不显眼的细节。实验结果表明，我们的方法的性能在数量上达到了最先进的水平。具体来说，我们方法的图像超分辨率结果具有更弱的纹理细节。