Recently, deep convolutional neural networks (CNNs) have been demonstrated
remarkable progress on single image super-resolution. However, as the depth and
width of the networks increase, CNN-based super-resolution methods have been
faced with the challenges of computational complexity and memory consumption in
practice. In order to solve the above questions, we propose a deep but compact
convolutional network to directly reconstruct the high resolution image from
the original low resolution image. In general, the proposed model consists of
three parts, which are feature extraction block, stacked information
distillation blocks and reconstruction block respectively. By combining an
enhancement unit with a compression unit into a distillation block, the local
long and short-path features can be effectively extracted. Specifically, the
proposed enhancement unit mixes together two different types of features and
the compression unit distills more useful information for the sequential
blocks. In addition, the proposed network has the advantage of fast execution
due to the comparatively few numbers of filters per layer and the use of group
convolution. Experimental results demonstrate that the proposed method is
superior to the state-of-the-art methods, especially in terms of time
performance.

本论文提出了一种深度但紧凑的卷积神经网络，通过特征提取、信息提炼和重构网络三部分组成，使用带有增强单元和压缩单元的信息提炼块提取局部长短路径特征，具有快速执行的优点，实验结果表明其在超分辨率方面优于现有方法，特别是时间表现。