  Super-Resolution convolutional neural networks have recently demonstrated
high-quality restoration for single images. However, existing algorithms often
require very deep architectures and long training times. Furthermore, current
convolutional neural networks for super-resolution are unable to exploit
features at multiple scales and weigh them equally, limiting their learning
capability. In this exposition, we present a compact and accurate
super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN).
The proposed network employs cascading residual on the residual structure to
allow the flow of low-frequency information to focus on learning high and
mid-level features. In addition, deep supervision is achieved via the densely
concatenated residual blocks settings, which also helps in learning from
high-level complex features. Moreover, we propose Laplacian attention to model
the crucial features to learn the inter and intra-level dependencies between
the feature maps. Furthermore, comprehensive quantitative and qualitative
evaluations on low-resolution, noisy low-resolution, and real historical image
benchmark datasets illustrate that our DRLN algorithm performs favorably
against the state-of-the-art methods visually and accurately.
