Semantic-Aware Guidance for Blind Super-Resolution of Remote Sensing Images

Unlike traditional super-resolution (SR) methods that rely on fixed degradation models, blind SR (BSR) methods can capture the complex processes introduced by factors such as sensor noise and platform motion in real-world remote sensing imagery. While most BSR methods effectively remove degradation from low-resolution (LR) images, they often struggle to preserve high-frequency details, leading to reduced reconstruction accuracy. To address this issue, we propose the semantic-aware guidance BSR (SGBSR) network, which leverages semantic information to guide the entire restoration process, enabling more accurate reconstruction. Specifically, we design a semantic extractor that utilizes powerful pretrained visual models to capture rich semantic information from LR images, which is then integrated into the SR network. To further enhance the network’s ability to handle complex degradations, we introduce an implicit estimation method. Subsequently, the semantic information and degradation representations are, respectively, incorporated into the SR network through the semantic-aware block (SaB) and the degradation-aware block (DaB). Experiments on both synthetic and real-world LR images demonstrate that our method achieves superior reconstruction accuracy.