Frequency-Aware Prompt Learning for Remote Sensing Image Super-Resolution

Abstract

Remote sensing image super-resolution is a critical task for reconstructing high-fidelity images from low-resolution observations. However, practical remote sensing scenarios often involve complex, compound degradations that severely compromise essential high-frequency structures, such as sharp edges and fine textures. While conventional convolutional neural network and Transformer-based methods have shown promise, they predominantly rely on static feature representations that lack adaptability to diverse scenes and often struggle to restore specific high-frequency details due to spectral bias. To this end, we propose a novel frequency-aware prompt learning framework. Specifically, a frequency-aware transformer module is designed to leverage the fast Fourier transform for explicit high-frequency component restoration, thereby overcoming the frequency bias inherent in spatial-domain models and effectively recovering intricate structural details. Concurrently, a dynamically generated prompt modulator is introduced to provide scene-specific adaptability via learnable vectors. This allows the network to adaptively calibrate feature responses to mitigate diverse environmental variations and compound artifacts. This synergistic integration ensures superior reconstruction fidelity and robust generalization across heterogeneous remote sensing scenarios. Comprehensive experiments across three benchmark datasets demonstrate that our method achieves outstanding performance in both quantitative metrics and visual quality assessments.