Deep learning-based single-image super-resolution reconstruction method has been relatively perfect. The reconstructed image has a high objective evaluation value or a good visual effect; however, the image perception effect and objective evaluation value cannot be improved in a balanced manner. To address this problem, this paper proposes a single-image super-resolution reconstruction method based on an attention fusion generative adversarial network. In the proposed method, first, the batch layer that destroys the original image contrast information and affects the quality of image generation in the residual network is removed. Then, the residual block of the attention convolutional neural network, which can effectively perform adaptive feature refinement in the feature map, is constructed. To improve the reconstruction results that lack high-frequency information and texture details under large-scale factors, a pixel-loss function is constructed to replace the mean squared error-loss function with a more robust Charbonnier loss function, and a total variation regular term is used to smooth the training results. The experimental results show that compared with other methods on the Set5, Set14, Urban100, and BSDS100 test sets under 4× magnification factor, the average peak signal-to-noise ratio and average structure similarity increased by 2.88 dB and 0.078, respectively. The experimental data and renderings demonstrate that the proposed method is subjectively rich in details, objectively has a high peak signal-to-noise ratio and structural similarity value, and achieves a balanced improvement of visual effects and objective evaluation index values.