关键词: 递归残差网络, 遥感图像超分辨率重建, 残差学习, 递归学习

Abstract: The deep network can effectively improve the accuracy of the reconstructed image, but it has a large number of parameters, which makes the training time too long. Therefore, this paper improves the super-resolution reconstruction algorithm of remote sensing image based on recursive residual network. The global residual learning and local residual learning are combined to effectively reduce the difficulty of training deep network and control the network parameters through recursive learning. The experimental results show that the recursive residual network is effective in the super-resolution reconstruction of the remote sensing image, and the improved network can obtain better subjective visual effect and objective evaluation index.

Key words: recursive residual network, remote sensing image super-resolution reconstruction, residual learning, recursive learning