Existing super-resolution (SR) networks typically capture multi-scale features by stacking multi-branch structures, leading to slow inference and limited modeling of global pixel dependencies. Some studies introduce Transformer-based self-attention to enhance reconstruction quality but at the cost of increased complexity. To address these challenges, this study proposes a novel SR network that combines multi-scale edge enhancement with a lightweight Transformer (ECTL-SR). A lightweight edge-guided convolutional block effectively captures and fuses fine-grained edge features under different receptive fields, while structural re-parameterization reduces redundant computation and memory overhead. A lightweight position-aware circular convolution is embedded into a modified Transformer to boost the network’s ability to model long-range dependencies and efficiently expand the receptive field at low cost. Experiments show that the proposed network achieves a good balance between performance and efficiency, outperforming existing SR methods on benchmark datasets such as Urban100 with superior reconstruction results.