  In most recent years, deep convolutional neural networks (DCNNs) based image
super-resolution (SR) has gained increasing attention in multimedia and
computer vision communities, focusing on restoring the high-resolution (HR)
image from a low-resolution (LR) image. However, one nonnegligible flaw of
DCNNs based methods is that most of them are not able to restore
high-resolution images containing sufficient high-frequency information from
low-resolution images with low-frequency information redundancy. Worse still,
as the depth of DCNNs increases, the training easily encounters the problem of
vanishing gradients, which makes the training more difficult. These problems
hinder the effectiveness of DCNNs in image SR task. To solve these problems, we
propose the Multi-view Aware Attention Networks (MAANet) for image SR task.
Specifically, we propose the local aware (LA) and global aware (GA) attention
to deal with LR features in unequal manners, which can highlight the
high-frequency components and discriminate each feature from LR images in the
local and the global views, respectively. Furthermore, we propose the local
attentive residual-dense (LARD) block, which combines the LA attention with
multiple residual and dense connections, to fit a deeper yet easy to train
architecture. The experimental results show that our proposed approach can
achieve remarkable performance compared with other state-of-the-art methods.
