SafaSR: An Arbitrary-Scale Image Super-Resolution Network Based on Multidomain Feature Fusion for Enhancing Diverse IoT Vision

The substantial heterogeneity in energy and communication protocols among IoT devices leads to highly diversified image resolutions, which severely constrain the reliability of downstream visual analysis tasks in applications like intelligent transportation and smart buildings. While recent deep learning-based super-resolution (SR) methods have achieved remarkable success, the majority are typically designed for specific integer scaling factors, requiring separate models for different scales, which is impractical for real-world IoT applications. To address this, we propose spatial-frequency domain image arbitrary-scale super-resolution (SafaSR), an arbitrary-scale image SR network based on multidomain feature fusion. Our key innovation lies in a multidomain multilevel feature fusion (M2F2) mechanism, which is driven by a scale-aware feature learning (SFL) model that adaptively extracts features from both spatial and frequency domains. The M2F2 mechanism is designed to reduce correlations between different feature domains, allowing a more effective integration of complementary information. Extensive experiments show that our proposed network outperforms the most advanced image SR algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics on the benchmark datasets, with fewer network parameters and less runtime.