Convolutional neural networks (CNNs) depend on deep network architectures to
extract accurate information for image super-resolution. However, obtained
information of these CNNs cannot completely express predicted high-quality
images for complex scenes. In this paper, we present a dynamic network for
image super-resolution (DSRNet), which contains a residual enhancement block,
wide enhancement block, feature refinement block and construction block. The
residual enhancement block is composed of a residual enhanced architecture to
facilitate hierarchical features for image super-resolution. To enhance
robustness of obtained super-resolution model for complex scenes, a wide
enhancement block achieves a dynamic architecture to learn more robust
information to enhance applicability of an obtained super-resolution model for
varying scenes. To prevent interference of components in a wide enhancement
block, a refinement block utilizes a stacked architecture to accurately learn
obtained features. Also, a residual learning operation is embedded in the
refinement block to prevent long-term dependency problem. Finally, a
construction block is responsible for reconstructing high-quality images.
Designed heterogeneous architecture can not only facilitate richer structural
information, but also be lightweight, which is suitable for mobile digital
devices. Experimental results shows that our method is more competitive in
terms of performance and recovering time of image super-resolution and
complexity. The code of DSRNet can be obtained at
this https URL

本文介绍了一种动态网络（DSRNet）用于图像超分辨率，它包含了残差增强块、宽增强块、特征细化块和构建块。DSRNet 不仅能提取更准确的图像超分辨率信息，还能适用于复杂场景，并具有更轻便的结构。