  Deep Convolutional Neural Networks (CNN) have drawn great attention in image
super-resolution (SR). Recently, visual attention mechanism, which exploits
both of the feature importance and contextual cues, has been introduced to
image SR and proves to be effective to improve CNN-based SR performance. In
this paper, we make a thorough investigation on the attention mechanisms in a
SR model and shed light on how simple and effective improvements on these ideas
improve the state-of-the-arts. We further propose a unified approach called
"multi-grained attention networks (MGAN)" which fully exploits the advantages
of multi-scale and attention mechanisms in SR tasks. In our method, the
importance of each neuron is computed according to its surrounding regions in a
multi-grained fashion and then is used to adaptively re-scale the feature
responses. More importantly, the "channel attention" and "spatial attention"
strategies in previous methods can be essentially considered as two special
cases of our method. We also introduce multi-scale dense connections to extract
the image features at multiple scales and capture the features of different
layers through dense skip connections. Ablation studies on benchmark datasets
demonstrate the effectiveness of our method. In comparison with other
state-of-the-art SR methods, our method shows the superiority in terms of both
accuracy and model size.
