  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.
