Shallow features play a pivotal role in super-resolution reconstruction networks, as they encompass intricate image details and offer explicit reference value for precise estimation of deep features. Nonetheless, researchers frequently disregard shallow features and excessively depend on deep module stacking and topology optimization, leading to redundant information. Consequently, this study introduces a lightweight super-resolution reconstruction network that endeavours to explore the mapping mechanism between shallow and deep features to enhance reconstruction quality. Firstly, shallow features are leveraged to generate feature masks that guide the generation process of deep features. Then, an attention-based feature selection module dynamically generates feature weight information. Finally, a dual-branch feature enhancement learning module is devised to balance the output feature weights and augment the feature fusion capability, thereby further improving the reconstruction performance. Experimental findings substantiate that the proposed algorithm significantly enhances the peak signal-to-noise ratio and structural similarity metrics on a widely used international dataset, while concurrently exhibiting a limited number of model parameters and exemplary visual performance. These outcomes validate the effectiveness and superiority of the lightweight super-resolution reconstruction network proposed in this study.