In recent years, the Transformer has demonstrated remarkable performance in image super-resolution tasks, attributed to its powerful ability to capture global features using a self-attention mechanism. However, this mechanism has high computational demands and is limited in its ability to capture local features. To address these challenges, this study proposes a lightweight image super-resolution reconstruction network based on dual-stream feature enhancement. This network incorporates a dual-stream feature enhancement module designed to enhance reconstruction performance through the effective capture and fusion of both global and local image information. In addition, a lightweight feature distillation module is introduced, which employs shift operations to expand the convolutional kernel's field of view, significantly reducing network parameters. The experimental results show that the proposed method outperforms traditional convolution-based reconstruction networks in terms of both subjective visual quality and objective metrics. Furthermore, compared to Transformer-based reconstruction networks such as SwinIR-Light and NGswin, the proposed method achieves an average improvement of 0.06 dB and 0.14 dB in PSNR, respectively.