Ancient murals are unique surviving copies and unique in artistic style. However, the effect of reconstructing missing information is poor as reference data are scarce. The key to improving the reconstruction process of murals is producing enough reference samples. In this study, an improved style transfer method is proposed to generate high-quality mural images. First, clustering is performed in the low-dimensional space of the input images to maintain their structural integrity. Second, a residual shrinkage module based on the attention mechanism and soft threshold function is introduced to remove redundant information in the images and effectively retain texture details. Finally, the content and matched style clustering sets are converted into real-time features to obtain migration images of any style. The experimental results show that the proposed method can generate natural and clear mural images as well as achieve better results in terms of peak signal-to-noise ratio and structural similarity than other common style transfer methods. Moreover, in an experiment of mural digitization based on a generative adversarial network, the superiority of the proposed method over the conventional augmentation method is verified.