This study proposes an efficient Mamba-Transformer collaborated network, termed MTSR, to address the challenges of balancing the global receptive field, local feature extraction, and computational efficiency in lightweight image super-resolution models, while tackling the insufficient cross-token interaction capability in Mamba-based context modeling. First, a hybrid synergy architecture is constructed by integrating Mamba and Transformer modules in an optimal ratio. This design leverages the Transformer’s superior cross-token interaction ability to compensate for the contextual modeling deficiencies in purely Mamba-based models. Second, a novel deep convolutional attention feed-forward network is designed to replace the traditional multi-layer perceptron. This network significantly enhances the capability for local detail extraction and effective inter-channel communication, thus reducing pixel-level information loss and amplifying the performance potential of the Mamba modules. Finally, a triple depthwise-separable shallow refinement block is introduced to efficiently capture and enhance the shallow-level features of an image, thus providing richer original texture information for subsequent nonlinear mapping. Extensive experiments on five public benchmark datasets demonstrate that the proposed MTSR model achieves peak signal-to-noise ratio (PSNR) gains of up to 0.31 and 0.38 dB over the state-of-the-art lightweight models SRFormer-light and MambaIR-light, respectively, while maintaining a highly competitive inference speed. This study validates that the proposed method provides an effective solution for the field of lightweight image super-resolution, combining both high performance and high efficiency.