The performance of image super-resolution (SR) have been greatly improved with deep convolution neural network (CNN). Despite image SR targets at recovering high-frequency details, most SR methods still focus on generating high-level features via a deep and wide network. They lack the discriminative ability of high-frequency information hidden in the abundant CNN features, thus hindering CNNs to yield better SR results. To tackle this issue, we propose two new attention mechanism: context weighted channel attention (CWCA) and persistent spatial attention (PSA). They modulate abundant features by suppressing the useless features and enhancing the interested ones in a channel-and-spatial manner. The network is then enabled to concentrate more on informative features closely related to the high-frequency components of an image. Furthermore, we propose enhanced attention residual groups with dense connection (EARG-D) to capture not only short-term information but also long-term information to maintain more useful features. Finally, we construct a deep enhanced context attention super resolution network (EASR) for better image reconstruction. Quantitative and qualitative experiments well demonstrate that our proposed method performs better than existing state-of-the-art SR methods.

中文翻译：

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用于图像超分辨率的增强型上下文关注网络

深度卷积神经网络（CNN）极大地提高了图像超分辨率（SR）的性能。尽管图像SR的目标是恢复高频细节，但大多数SR方法仍专注于通过深度和广泛的网络生成高级功能。它们缺乏隐藏在丰富的CNN功能中的高频信息的判别能力，因此阻碍了CNN产生更好的SR结果。为解决此问题，我们提出了两种新的注意力机制：上下文加权频道注意力（CWCA）和持久性空间注意力（PSA）。它们通过抑制无用的特征并以通道和空间的方式增强感兴趣的特征来调制丰富的特征。然后使网络能够将更多的精力集中在与图像的高频分量紧密相关的信息特征上。此外，我们提出了具有密集连接（EARG-D）的增强注意力残差组，不仅可以捕获短期信息，还可以捕获长期信息以保持更有用的功能。最后，我们构建了一个深度增强的上下文关注超分辨率网络（EASR），以实现更好的图像重建。定量和定性实验很好地证明了我们提出的方法比现有的最新SR方法具有更好的性能。