Abstract
Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has
not been fully exploited in existing deep learning based
image SR methods. In this paper, we propose an image
super-resolution feedback network (SRFBN) to refine lowlevel representations with high-level information. Specifi-
cally, we use hidden states in a recurrent neural network
(RNN) with constraints to achieve such feedback manner.
A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution
image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable
for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of
the proposed SRFBN in comparison with the state-of-theart methods. Code is avaliable at https://github.
com/Paper99/SRFBN_CVPR19.