Inception residual attention network for remote sensing image super-resolution

How to enhance the spatial resolution for a remote sensing image is an important issue that we face. Many image super-resolution (SR) techniques have been proposed for this purpose and deep convolutional neural network (CNN) is the most effective approach in recent years. However, we observe that most CNN-based SR methods treat low-frequency areas and high-frequency areas equally, hence hindering the recovery of high-frequency information. In this paper, we propose a network named inception residual attention network (IRAN) to address this problem. Specifically, we propose a spatial attention module to make the network adaptively learn the importance of different spatial areas, so as to pay more attention to the areas with high-frequency information. Furthermore, we present an inception module to fuse local multilevel features, so as to provide richer information for reconstructing detailed textures. In order to evaluate the effectiveness of the proposed method, a large number of experiments are performed on UCMerced-LandUse data set and the results show that the proposed method is superior to the current state-of-the-art methods in both visual effects and objective indicators.