🤖 AI Summary
Existing image super-resolution methods struggle to balance reconstruction quality and model complexity. To address this challenge, this work proposes a lightweight Multi-scale Spatially Adaptive Attention Network (MSAAN), whose core component is the Multi-scale Spatially Adaptive Attention (MSAA) module. The MSAA module integrates Global Feature Modulation (GFM) and Multi-scale Feature Aggregation (MFA), complemented by a Local Enhancement Block (LEB) and a Feature Interaction Gated Feed-Forward (FIGFF) module to efficiently model both local details and long-range dependencies. Extensive experiments demonstrate that MSAAN and its lightweight variant achieve state-of-the-art or competitive performance in terms of PSNR and SSIM across multiple benchmark datasets, while significantly reducing model parameters and computational overhead.
📝 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.