Deep neural networks have demonstrated remarkable reconstruction for single-image super-resolution (SISR). However, most existing CNN-based SISR methods directly learn the relation between low-resolution (LR) and high-resolution (HR) images, neglecting to explore the recurrence of internal patches, hence hindering the representational power of CNNs. In this paper, we propose a novel single image Super-Resolution network based on Graph ATtention network (SRGAT) to make full use of the internal patch-recurrence in a natural image. The proposed model employs a feature mapping block with a recurrent structure to refine low-level representations with high-level information. Especifically, the feature mapping block contains a parallel graph similarity branch and a content branch, where the graph similarity branch aims at exploiting the similarity and symmetry across different image patches in low-resolution feature space and provides additional priors for the content branch to enhance texture details. Specifically, we consider the internal patch-recurrence of an image by constructing a graph network on image feature patches. In this way, the information from neighboring patches can be interacted using graph attention network (GAT) to help it recover additional textures, which complements the textures learned from the content branch. Extensive quantitative and qualitative evaluations on five benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art super-resolution methods.

中文翻译：

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SRGAT：具有图注意网络的单图像超分辨率


深度神经网络已经证明了单图像超分辨率（SISR）的卓越重建能力。然而，大多数现有的基于 CNN 的 SISR 方法直接学习低分辨率（LR）和高分辨率（HR）图像之间的关系，忽略了探索内部补丁的重复性，从而阻碍了 CNN 的表示能力。在本文中，我们提出了一种基于图注意网络（SRGAT）的新型单图像超分辨率网络，以充分利用自然图像中的内部补丁循环。所提出的模型采用具有循环结构的特征映射块来用高级信息细化低级表示。具体来说，特征映射块包含并行图相似性分支和内容分支，其中图相似性分支旨在利用低分辨率特征空间中不同图像块的相似性和对称性，并为内容分支提供额外的先验以增强纹理细节。具体来说，我们通过在图像特征块上构建图网络来考虑图像的内部块循环。通过这种方式，来自相邻补丁的信息可以使用图注意网络（GAT）进行交互，以帮助它恢复额外的纹理，这补充了从内容分支学习的纹理。对五个基准数据集的广泛定量和定性评估表明，所提出的算法优于最先进的超分辨率方法。