Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement. However, most CNN-based methods mainly focus on feed-forward architecture design and neglect to explore the feedback mechanism, which usually exists in the human visual system. In this paper, we propose feedback pyramid attention networks (FPAN) to fully exploit the mutual dependencies of features. Specifically, a novel feedback connection structure is developed to enhance low-level feature expression with high-level information. In our method, the output of each layer in the first stage is also used as the input of the corresponding layer in the next state to re-update the previous low-level filters. Moreover, we introduce a pyramid non-local structure to model global contextual information in different scales and improve the discriminative representation of the network. Extensive experimental results on various datasets demonstrate the superiority of our FPAN in comparison with the state-of-the-art SR methods.

翻译：最近,基于革命性神经网络(CNN)的图像超分辨率(SR)方法取得了显著的性能改进,然而,以CNN为基础的大多数方法主要侧重于向导结构设计和忽视,以探索通常存在于人类视觉系统中的反馈机制;在本文件中,我们提出反馈金字塔关注网络(FPAN),以充分利用各种特征的相互依存性;具体地说,正在开发一个新的反馈连接结构,以加强高层次信息的低层次特征表达;在我们的方法中,第一阶段的每个层的产出也被用作下一个州相应层的投入,以更新以前的低层次过滤器;此外,我们引入一个金字塔非本地结构,以建模不同规模的全球背景信息模型,并改进网络的歧视性代表性;关于各种数据集的广泛实验结果显示我们的FPAN与最先进的SR方法相比的优势。