A Progressive Feature Enhancement Deep Network for Large-Scale Remote Sensing Image Superresolution
Yao Wang, Weiwei Liu, Weiwei Sun, Xiangchao Meng, Gang Yang, Jiancheng Li
Abstract
The pursuit of super-resolution (SR) with large upscaling factors such as 8×, for enhancing the spatial resolution of low-resolution (LR) remote sensing images is a persistent and challenging problem. To address this issue, we propose the Progressive Feature Enhancement SR (PFESR) network with an 8× upscaling factor. Given the limited high-frequency information provided by a single LR image, we propose an improved style transfer technology to generate auxiliary details that aid in the recovery of high-resolution (HR) images. Additionally, multi-scale texture features are extracted through the Visual Geometry Group (VGG) feature extraction (VFE) block. To efficiently fuse various features, we combine hard and soft attention mechanisms. Finally, we use a hierarchical fusion block to address the progressive fusion problem of multiple scale features. Experiments on three datasets demonstrate that our method achieves state-of-the-art performance and exhibits good robustness in 8× and higher scale SR tasks.