Fused Recurrent Network via Channel Attention for Remote Sensing Satellite Image Super-Resolution

Remote sensing satellite images often suffer from low spatial resolution. Image super-resolution plays an important role in remote sensing image processing. However, existing methods show that increasing network depth will inevitably lead to the dramatic increase of model parameters and the overfitting problem. Besides, most methods treat different types of information (low-frequency and high-frequency) equally. Motivated by these observations, we propose a fused recurrent network via channel attention (CA-FRN) in this paper. The basic module, recursive channel attention block (RCAB), pays enough attention to the high-frequency information and diminishes the low-frequency information adaptively through channel attention. Based on RCAB, we render our model effective by retaining and fusing hierarchical local information of both low-resolution and high-resolution, and we enhance the network performance simply by increasing the number of RCABs without adding extra parameters. We evaluate the proposed model on satellite images from different datasets, and the proposed CA-FRN is superior to the state-of-the-art methods. Code is available at https://github.com/lxy0922/CAFRN.