Gated Multi-Scale Interaction Networks for Efficient Single Image Super-Resolution

Single image super-resolution (SR) aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Although recent transformer-based SR methods have achieved impressive performance, their substantial computational complexity and memory requirements severely restrict practical deployment. To address these challenges, we propose the gated multi-scale interaction network (GMIN), a lightweight convolutional neural network architecture that effectively integrates transformer design principles. GMIN introduces the gated multi-scale interaction module, which comprises a spatially adaptive mixing layer (SML) and an enhanced spatial gated feed-forward network (EGSFN). The SML dynamically filters less informative features and aggregates multi-scale spatial information through innovative gating mechanisms, while EGSFN employs large-kernel convolutions with gating operations to capture rich spatial dependencies, significantly enhancing feature representation capabilities. Comprehensive experimental results demonstrate that GMIN achieves an exceptional balance between SR quality and computational efficiency, outperforming the transformer-based ESRT by 0.14 dB in peak signal-to-noise ratio while utilizing fewer parameters and requiring 77% fewer floating-point operations per second. These findings establish GMIN as a novel and practical solution for lightweight image SR, making a significant contribution to the development of efficient SR models suitable for resource-constrained environments.