A single-image super-resolution reconstruction method based on aggregated residual attention network is proposed to solve the problems for insufficient feature information mining, high algorithm complexity, and unstable training in the super-resolution reconstruction for a single image in existing generative countermeasure networks. First, the aggregated residual module is used as the basic residual block to construct a generator, to reduce computational complexity. In each residual block, an attention module with a three-dimensional weight is introduced as the main channel to capture additional high-frequency information without other parameters. Second, the discriminator network parameters are limited via spectral normalization to stabilize the training process. Finally, the Swish activation function with improved fitting is used to improve the feature extraction ability of the network. The Charbonnier loss function with enhanced robustness is used as the pixel loss, and the regularization loss is added to suppress image noise to improve spatial smoothness. The experimental results show that the average value of the peak signal-to-noise ratio and structural similarity of images reconstructed using the proposed method on Set5, Set14, and BSD100 public datasets increase by 1.54 dB and 0.0457, respectively. Therefore, the reconstructed images have a better resolution and richer high-frequency detail than the original image.