Comic style transfer based on generative confrontation network

Style transfer refers to extracting styles from a specified style image template, and mapping the extracted style features to the content image without destroying the image content. At present, the use of deep learning methods for style transfer is one of the hotspots in the field of image research. This paper implements an image style transfer architecture based on cyclic consistent generative adversarial network, i. e., cyclic consistent confrontation network, and uses densely connected convolutional network to deepen the number of network layers. Test results show that stylized image with the proposed method is more obvious than that of the original cyclic consistent generative adversarial network, and the image quality is improved apparently.