In the past decade, single image super-resolution (SISR) based on convolutional neural networks (CNNs) has been represented remarkable performance. Powerful characterization of CNN is important for recent methods to learn an intricate non-linear mapping between high-resolution and corresponding low-resolution images. However, a deeper and wider network structure brings superior performance while increasing the number of network parameters and calculations so that it is difficult to handle the real-time information. Hence, it can be embedded in mobile devices with difficulty. Inspired by the above motivation, a lightweight network for the real-time SISR is proposed by stacking efficient cascading residual blocks, which consist of several concatenate effective modules with wide activation. To further improve the network performance, with the increase of a slight number of parameters, the proposed network cooperates with a lightweight residual efficient channel attention module to capture feature interaction between channels. Extensive experiments provide significant demonstrations that the proposed network obtains the superior trade-off between performance and parameters compared with other current methods. The lightweight trait of our method allows it to implement real-time image processing and can be embedded in mobile devices.

在过去的十年中，基于卷积神经网络 (CNN) 的单图像超分辨率 (SISR) 已表现出卓越的性能。CNN 的强大表征对于最近学习高分辨率和相应低分辨率图像之间复杂的非线性映射的方法很重要。然而，更深、更宽的网络结构带来了优越的性能，同时增加了网络参数和计算量，难以处理实时信息。因此，它很难嵌入到移动设备中。受上述动机的启发，通过堆叠有效的级联残差块，提出了一种用于实时 SISR 的轻量级网络，该残差块由几个具有广泛激活的串联有效模块组成。为了进一步提高网络性能，随着少量参数的增加，所提出的网络与轻量级残差高效通道注意模块配合以捕获通道之间的特征交互。大量实验提供了重要的证明，与其他当前方法相比，所提出的网络在性能和参数之间获得了优越的权衡。我们方法的轻量级特性使其能够实现实时图像处理并可以嵌入到移动设备中。大量实验提供了重要的证明，与其他当前方法相比，所提出的网络在性能和参数之间获得了优越的权衡。我们方法的轻量级特性使其能够实现实时图像处理并可以嵌入到移动设备中。大量实验提供了重要的证明，与其他当前方法相比，所提出的网络在性能和参数之间获得了优越的权衡。我们方法的轻量级特性使其能够实现实时图像处理并可以嵌入到移动设备中。