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Single-image super-resolution (SISR) methods based on convolutional neural networks (CNNs) have achieved breakthrough progress in reconstruction quality. However, their high computational costs and model complexity have limited their applications in resource-constrained devices. To address this, we propose the MSWSR (multi-scale wavelet super-resolution) method, a lightweight multi-scale feature selection network that exploits both symmetric and asymmetric feature patterns. MSWSR achieves efficient feature extraction and fusion through modular design. The core modules include a mixed feature module (MFM) and a gated attention unit (GAU). The MFM employs a symmetric multi-branch structure to efficiently integrate multi-scale features and enhance low-frequency information modeling. The GAU combines the spatial attention mechanism with the gating mechanism to further optimize symmetric feature representation capability. Moreover, a lightweight spatial selection module (SSA) adaptively assigns weights to key regions while maintaining structural symmetry in feature space. This significantly improves reconstruction quality in complex scenes. In 4× super-resolution tasks, compared to SPAN, MSWSR improves PSNR by 0.22 dB on Urban100 and 0.26 dB on Manga109 datasets. The model contains only 316K parameters, which is substantially lower than existing approaches. Extensive experiments demonstrate that MSWSR significantly reduces computational overhead while maintaining reconstruction quality, providing an effective solution for resource-constrained applications. Full article