  Deep neural networks have exhibited promising performance in image
super-resolution (SR) due to the power in learning the non-linear mapping from
low-resolution (LR) images to high-resolution (HR) images. However, most deep
learning methods employ feed-forward architectures, and thus the dependencies
between LR and HR images are not fully exploited, leading to limited learning
performance. Moreover, most deep learning based SR methods apply the pixel-wise
reconstruction error as the loss, which, however, may fail to capture
high-frequency information and produce perceptually unsatisfying results,
whilst the recent perceptual loss relies on some pre-trained deep model and
they may not generalize well. In this paper, we introduce a mask to separate
the image into low- and high-frequency parts based on image gradient magnitude,
and then devise a gradient sensitive loss to well capture the structures in the
image without sacrificing the recovery of low-frequency content. Moreover, by
investigating the duality in SR, we develop a dual reconstruction network (DRN)
to improve the SR performance. We provide theoretical analysis on the
generalization performance of our method and demonstrate its effectiveness and
superiority with thorough experiments.
