🤖 AI Summary
Single-image super-resolution (SISR) models typically exhibit poor generalization across diverse scaling factors.
Method: We propose a plug-and-play Scale-Aware Attention Module (SAAM)—a lightweight, parameter-free design that introduces the first scale-adaptive attention mechanism. SAAM is integrated with a gradient variance loss to enhance texture sharpness and is compatible with mainstream SISR backbones (e.g., SCNet, HiT-SR, OverNet). Crucially, it enables fixed-scale models to perform arbitrary integer or non-integer upscaling without retraining.
Results: Extensive evaluations on standard benchmarks demonstrate that our approach significantly improves cross-scale robustness and real-world applicability while maintaining low computational overhead. It achieves state-of-the-art performance across multiple metrics and scales, outperforming existing methods in both quantitative accuracy and qualitative fidelity.
📝 Abstract
Despite remarkable progress in Single Image Super-Resolution (SISR), traditional models often struggle to generalize across varying scale factors, limiting their real-world applicability. To address this, we propose a plug-in Scale-Aware Attention Module (SAAM) designed to retrofit modern fixed-scale SR models with the ability to perform arbitrary-scale SR. SAAM employs lightweight, scale-adaptive feature extraction and upsampling, incorporating the Simple parameter-free Attention Module (SimAM) for efficient guidance and gradient variance loss to enhance sharpness in image details. Our method integrates seamlessly into multiple state-of-the-art SR backbones (e.g., SCNet, HiT-SR, OverNet), delivering competitive or superior performance across a wide range of integer and non-integer scale factors. Extensive experiments on benchmark datasets demonstrate that our approach enables robust multi-scale upscaling with minimal computational overhead, offering a practical solution for real-world scenarios.