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.

介绍了一种基于 Residual Laplacian Network 的超分辨率算法，利用多尺度特征，并实现了深度监督学习和 Laplacian attention 机制，  在低分辨率，噪声低分辨率和真实历史图像基准数据集上得到了较好的视觉和准确性表现。