Arbitrary-Scale Super-Resolution for Remote Sensing Images with Multi-Branch Feature Enhancement and Scale-Specific Dictionary Attention

For remote sensing image processing, high quality images are particularly essential because of their more detailed texture information and clearer edges. Image super-resolution SR could reconstruct the high-resolution HR image from its low-resolution LR counterpart, overcoming the limitations of devices and environmental conditions. The shortcoming of most SR methods is that they could only be applied to fixed-scale SR task, which requires more training and deployment cost. Therefore, arbitrary-scale SR approaches are proposed to restore HR images of different scales with a single model. However, most approaches only use simple MLPs or the local attention mechanism in the decoding phase, which limits the representative power of the model. In this work, we propose an arbitrary-scale super-resolution method for remote sensing images with Multi-branch Feature Enhancement and Scale-specific Dictionary Attention MFESDA . We use a Multi-branch Feature Enhancement MFE module which combines global information and scale-aware attention to capture more informative features. Moreover, we design a Scale-specific Multi-level Dictionary Attention Modulation SMDAM module in the decoding process which makes use of scale-specific priors to improve the performance. The experimental results have shown that the proposed model performs better than other arbitrary-scale SR approaches and our visual quality is higher than other approaches.