Arbitrary scale super resolution network for satellite imagery
Jing Zhong Fang, Jing Xiao, Xu Wang, Dan Chen, Ruimin Hu · China Communications · 2022
Recently, satellite imagery has been widely applied in many areas. However, due to the limitations of hardware equipment and transmission bandwidth, the images received on the ground have low resolution and weak texture. In addition, since ground terminals have various resolutions and real-time playing requirements, it is essential to achieve arbitrary scale super-resolution (SR) of satellite images. In this paper, we propose an arbitrary scale SR network for satellite image reconstruction. First, we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs. Second, we design an edge reinforcement module to enhance the high-frequency details in satellite images through a two-branch network. Finally, extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.