Intelligent Algorithm for Ceramic Decorative Pattern Style Transfer Based on CycleGAN

Image style conversion refers to a technique that uses an algorithm to learn the style of a famous painting and then applies it to another picture. Current generative adversarial networks have been widely used for image style conversion. However, Cycle Generative Adversarial Networks (CycleGAN) is not very clear in processing image textures. This paper proposes a method of adding a Local Binary Pattern (LBP) algorithm and adds a cyclic LBP algorithm to the generator that generates the adversarial network to improve the effectiveness of the cyclically created adversarial network to extract image texture feature content. Experimental results show that adding the LBP algorithm can improve the image quality and make the trueness and falseness between the input picture and the picture generated by cyclegan closer.