Image style migration refers to learning the style of oil painting pictures and applying the learned style to other pictures to make the pictures have the style of oil painting. The current methods based on generative adversarial networks have been widely used in image style migration. Aiming at the problem that Cycle Generative Adversarial Networks (CycleGAN) do not have high texture definition when processing images, a method of adding a Local Binary Pattern (LBP) algorithm is proposed. The LBP algorithm is added into the generation model of CycleGAN to enhance the extraction of image texture features by CycleGAN. Aiming at the problem of noise in the generated images, Total Variation Loss is added into the loss function to constrain the noise. The experimental results show that the quality of the generated images can be improved by adding LBP algorithm and Total Variation Loss, and the generated images have better visual effects.