WFA-SRNet: A Wavelet-Guided and Feature-Aware Network for Remote Sensing Image Super-Resolution

Recently, deep learning-based remote sensing image super-resolution (RSISR) methods have achieved remarkable progress. However, effectively preserving high-frequency details remains a significant challenge, as these features are critical for downstream tasks such as object detection, change analysis, and scene classification. Moreover, relying solely on the information contained in low-resolution images often results in the loss of structural details, thereby degrading reconstruction quality. To address these issues, we propose a novel Wavelet-guided and Feature-Aware Super-Resolution Network (WFA-SRNet). The proposed network adopts a dual-branch architecture, consisting of a Feature Extraction Block (FEB) and a High-Frequency Extraction Block (HFE), to collaboratively model semantic structures and fine-grained textures. Specifically, FEB integrates a Shift-Window Cross Attention (SWCA) mechanism and a dictionary-based similarity matching strategy to capture non-local self-similarities, while the HFE branch incorporates a wavelet-domain high-frequency modeling module (WD-HFE), which explicitly decomposes and reconstructs frequency components via Discrete Wavelet Transform (DWT) and Inverse DWT (IDWT) to enhance edge and texture recovery. Furthermore, a Fusion Attention (FA) module is designed to guide the integration of multi-source features from both semantic and high-frequency pathways. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that WFA-SRNet achieves superior reconstruction performance, particularly in restoring structural and textural details. Additionally, the proposed method significantly improves the accuracy of downstream classification tasks, showing strong potential for practical RSISR applications.