Low Complexity Single Image Super-Resolution With Channel Splitting And Fusion Network

Recently, deep convolutional neural networks (CNNs) have made remarkable progress on single image super-resolution (SISR). However, many of these methods use very deep or wide convolutional layers to achieve good performance, which treat all feature channels indiscriminately and neglect the difference among the contribution of each channel to the output results. In this paper, we propose a low complexity solution based on channel splitting and fusion network (CSFN) to address this problem. Our method uses channel splitting and channel fusion to enhance feature maps and make full use of valuable information, and then multiple residual channel splitting and fusion blocks (CSFB) are cascaded to continuously extract more important information for reconstruction. To further minimize redundant parameters and improve efficiency, we adopt group and recursive convolutional layer strategy in CSFB. Experiments demonstrate that our proposed CSFN could achieve higher performance with low computational complexity than most state-of-the-art methods.