Residual Shuffle Attention Network for Image Super-Resolution
Zhiwei Li, Yaping Zhang, Yuwei Yang · Journal of Physics Conference Series · 2021
Abstract In order to improve the accuracy of the super-resolution network and reduce the number of model parameters, this paper improves its RCAB module on the basis of RCAN, and builds a reconstruction network RSAN that can improve the quality and efficiency of image super-resolution reconstruction. By replacing the original channel attention module with a more efficient and lightweight shuffle attention, it is mainly used to reduce the number of parameters, supplemented by improving the accuracy; and replacing part of the ordinary convolution in RCAN with split convolution is mainly used to improve accuracy, supplemented by reducing feature redundancy and parameters. The experimental results show that RSAN in this paper can not only obtain better subjective visual evaluation and objective quantitative evaluation, but also reduce the number of network model parameters and improve the efficiency of the network to a certain extent.