Lightweight remote sensing image super-resolution with sequential multi-scale feature enhancement network
Ailing Qi, Qi Shuai · The Imaging Science Journal · 2024
Remote sensing images possess abundant texture features and significant autocorrelation. However, the extensive network parameters and high computational demands of current super-resolution (SR) methods make them challenging to implement on mobile devices. This work proposes a lightweight model named the Sequential Multi-Scale Feature Enhancement Network (SMFEN) that address the issue on single remote sensing image super-resolution. Our sequential structure allows for a larger receptive field (RF) with minimal parameters, which can gradually build complex high-level multi-scale feature representations from simple low-level features, realizing the feature extraction process from concrete to abstract. In addition, we design a high-frequency multi-scale attention block which use multi-scale high-frequency details to facilitate the fusion of contextual information across different scales and effectively recover texture and edge information. Comprehensive experimental results demonstrate that our SMFEN network outperforms the latest lightweight SR methods.