Video surveillance, military object recognition, consumer photography, and many other fields have high requirements for image sharpness. In recent years, deep neural networks have made great progress in the applied research on visual and quantitative evaluation, but the results generally lack the details of image textures, and the edges are too smooth, providing blurry visual experience. For this reason, we propose a method of improving image sharpness based on the generative adversarial network in this study. In order to better delivery the image details, this method adopts the improved residual block and skip connection as the main structure of the generative network, and the generator loss function consists of content loss, perception loss, and texture loss in addition to adversarial loss. Finally, the experiments on the DIV2K dataset prove that the proposed method exhibits good visual experience and quantitative evaluation in terms of improving image sharpness.