Multilayer Degradation Representation-Guided Blind Super-Resolution for Remote Sensing Images

Remote sensing image super-resolution aims to boost the image resolution while recovering rich high-frequency details. Currently, most of super-resolution methods are based on an assumption that the degradation kernel is a specific downsampler. However, the degradation kernel is unknown and sophisticated for real remote sensing scenes, leading to a severe performance drop. To alleviate this problem, we propose a multi-layer degradation representation-guided blind super-resolution method for remote sensing images, which mainly consists of three key steps. First, an unsupervised representation learning is exploited to learn the degradation representation from low-resolution images. Then, a degradation-guided deep residual module is designed to model high-order features across different scales from original images. Finally, a multi-layer degradation-aware feature fusion mechanism is proposed to restore the finer details. Experiments on synthetic and real datasets demonstrate that the proposed method can achieve promising performance with respect to other state-of-the-art super-resolution approaches.