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 https://github.com/hellloxiaotian/DSRNet.

翻译：卷积神经网络（CNN）依赖深层网络架构提取图像超分辨率的准确信息。然而，这些CNN获取的信息无法完全表达复杂场景下预测的高质量图像。本文提出一种用于图像超分辨率的动态网络（DSRNet），该网络包含残差增强块、宽增强块、特征精炼块和重构块。残差增强块采用残差增强架构，以促进图像超分辨率的层级特征。为增强所获超分辨率模型对复杂场景的鲁棒性，宽增强块通过动态架构学习更鲁棒的信息，以提升模型对不同场景的适用性。为防止宽增强块中各组件的相互干扰，精炼块采用堆叠架构精确学习所获特征。同时，精炼块中嵌入残差学习操作以解决长期依赖问题。最后，重构块负责重建高质量图像。所设计的异构架构不仅能促进更丰富的结构信息，还能保持轻量化特性，适用于移动数字设备。实验结果表明，本方法在图像超分辨率的性能表现、恢复时间及复杂度方面更具竞争力。DSRNet代码可从https://github.com/hellloxiaotian/DSRNet获取。