Aiming at how to recover the texture details of the reconstructed super-resolution image, an image super-resolution reconstruction based on the self-attention generative adversarial network(SRAGAN) is proposed. In the SRAGAN, a generator based on a combination of the self-attention mechanism and the residual module is used to transform low-resolution into super-resolution images, while a discriminator based on the deep convolutional network tries to distinguish the difference between the reconstructed and real super-resolution images. In terms of loss function construction, on the one hand, the Charbonnier content loss function is used to improve the accuracy of image reconstruction; on the other hand, the eigenvalues before the activation layer in the pre-trained VGG network are used to calculate the perceptual loss to achieve accurate texture detail reconstruction of super-resolution images. Experiments show that the proposed SRAGAN is superior to the current popular algorithms in peak signal-to-noise ratio and structural similarity score, reconstructing more realistic images with clear textures.