FASwinNet: Frequency-Aware Swin Transformer for Remote Sensing Image Super-Resolution via Enhanced High-Similarity-Pass Attention and Octave Residual Blocks
Source abstract
Abstract
Remote Sensing Image Super-Resolution (RSISR) plays a vital role in enhancing the spatial details and interpretability of satellite imagery. However, existing methods often struggle to recover fine textures and high-frequency information effectively. In this paper, we propose a frequency-aware super-resolution network for remote sensing images, termed FASwinNet. The network introduces an Enhanced High-Similarity-Pass Attention (EHSPA) module, which improves high-frequency detail modeling through a similarity-aware mechanism guided by edge and positional information. Additionally, we design an Octave-based Residual Attention Block that explicitly separates and optimizes high- and low-frequency features, further enhancing texture reconstruction. Experimental results demonstrate that FASwinNet outperforms state-of-the-art methods in both visual quality and quantitative metrics, achieving the best PSNR and SSIM performance on the AID and UCMerced datasets.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Processing Techniques · Advanced Image Fusion Techniques · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Funding
Foundation of Liaoning Educational Committee · Liaoning Applied Basic Research Program
References (41)
Show source-provided references
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