让超分辨率模型轻松应对任意缩放比例，无需重新训练。
Your Super Resolution Model is not Enough for Tackling Real-World Scenarios
- 引入自适应注意力模块，动态调整不同缩放倍数下的特征提取。
- 在多个基准数据集上实现跨尺度性能超越，非整数缩放效果尤佳。
- 轻量级设计，可无缝嵌入现有模型，适合实际部署场景。
尽管单图像超分辨率（SISR）取得显著进展，传统模型在不同缩放因子间泛化能力差，限制了其实际应用。为此，我们提出一种即插即用的尺度感知注意力模块（SAAM），用于为固定缩放模型赋予任意尺度超分辨率能力。SAAM采用轻量级、自适应的特征提取与上采样机制，并结合无参数注意力模块（SimAM）进行高效引导，以及梯度方差损失以增强细节锐度。该方法可无缝集成至多种先进主干网络（如SCNet、HiT-SR、OverNet），在整数与非整数缩放因子下均表现优异或领先。大量实验表明，本方法在多个基准数据集上实现了稳健的多尺度上采样，且计算开销极低，为真实场景提供了实用解决方案。
原文摘要 · Abstract (English)
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.
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