In the digital era, significant challenges are posed to public safety and information credibility by low-resolution images. Existing super-resolution models are hindered by excessive parameters and high computational complexity, making them difficult to be deployed on edge devices, while deep network architectures often lead to the loss of high-frequency details. To address these issues, an efficient convolutional image super-resolution network integrated with a dual attention mechanism was proposed. A lightweight channel attention module and an enhanced spatial attention module were constructed to achieve cooperative optimization of dynamic channel-wise weight allocation and spatial key region enhancement. Combined with a partial convolution-based feed-forward network, the local feature extraction range was constrained, effectively suppressing interference from redundant receptive field expansion on texture reconstruction. The end-to-end architecture was designed with a single global residual connection and modular structure, which reduced parameters while enhancing cross-scale feature fusion capability. Experimental results demonstrate that the proposed model achieves 22 optimal results out of 30 core metrics across five benchmark datasets including Set5 and Set14, with an average PSNR(peak signal-to-noise ratio) improvement of 0.13 dB and an average parameter reduction of 37.5%. Local attribution map analysis further confirms that the model maintains a larger effective receptive field while key pixel information is utilized more precisely, significantly improving the reconstruction quality of high-frequency details. These findings adequately validate the superior efficiency-performance balance of the proposed model.