Abstract
This paper introduces a lightweight image super-resolution (SR) network, termed the Multi-scale Spatial Adaptive Attention Network (MSAAN), to address the common dilemma between high reconstruction fidelity and low model complexity in existing SR methods. The core of our approach is a novel Multi-scale Spatial Adaptive Attention Module (MSAA), designed to jointly model fine-grained local details and long-range contextual dependencies. The MSAA comprises two synergistic components: a Global Feature Modulation Module (GFM) that learns coherent texture structures through differential feature extraction, and a Multi-scale Feature Aggregation Module (MFA) that adaptively fuses features from local to global scales using pyramidal processing. To further enhance the network's capability, we propose a Local Enhancement Block (LEB) to strengthen local geometric perception and a Feature Interactive Gated Feed-Forward Module (FIGFF) to improve nonlinear representation while reducing channel redundancy. Extensive experiments on standard benchmarks (Set5, Set14, B100, Urban100, Manga109) across $\times2$, $\times3$, and $\times4$ scaling factors demonstrate that both our lightweight (MSAAN-light) and standard (MSAAN) versions achieve superior or competitive performance in terms of PSNR and SSIM, while maintaining significantly lower parameters and computational costs than state-of-the-art methods. Ablation studies validate the contribution of each component, and visual results show that MSAAN reconstructs sharper edges and more realistic textures.
Abstract (translated)
本文介绍了一种轻量级图像超分辨率（SR）网络，称为多尺度空间自适应注意力网络（MSAAN），旨在解决现有SR方法中高重建保真度与低模型复杂性之间的常见难题。我们的方法核心是一个新颖的多尺度空间自适应注意模块（MSAA），设计用于同时建模精细的局部细节和长程上下文依赖关系。MSAA包括两个协同组件：一个全局特征调制模块（GFM），通过差异特征提取来学习连贯的纹理结构，以及一个多尺度特性聚合模块（MFA），使用金字塔处理自适应地融合从局部到全球尺度的特征。 为了进一步增强网络的能力，我们提出了一种局部增强块（LEB）以强化局部几何感知，并提出了一个特性交互门控前馈模块（FIGFF）以改进非线性表示同时减少通道冗余。在标准基准测试集（Set5、Set14、B100、Urban100、Manga109）上进行了广泛的实验，涵盖$\times2$、$\times3$和$\times4$缩放因子，结果表明我们的轻量级版本（MSAAN-light）和标准版本（MSAAN）在PSNR和SSIM指标上的表现优于或与最先进的方法相当，同时保持了远低于这些方法的参数数量和计算成本。消融研究表明每个组件的重要性，并且视觉结果显示MSAAN重建出更清晰的边缘和更具现实感的纹理。
URL
https://arxiv.org/abs/2603.00118
