Efficient local cascading residual network for real-time single image super-resolution

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.