Deep Learning Algorithm for Woodcut Prints Style Transfer

In order to make the style transferred result of woodcut prints shows a more obvious woodcut nick texture,while maintaining the rationality of the nick texture distribution,a woodcut prints style transfer algorithm based on neural network semantic segmentation algorithm and neural style transfer is proposed,which transfers woodcut prints style according to different regions.Firstly,neural network segmentation algorithm and Labelme image annotation tool are used to segment the content image and the woodcut prints image respectively.Then binarization process the segmentation results to formed mask images.The mask images are used as guidances,and input into the neural style transfer network with spatial guidance channels for regional style transfer together with the content image and the woodcut prints image.Under the deep learning framework of PyTorch,the algorithm is used to transfer a large number of images of characters and natural scenes into woodcut prints,and the transferred results are compared with those of the state-of-art neural style transfer algorithms including iterative optimization,fast style transfer and arbitrary style transfer.Experiments show that the proposed woodcut prints style transfer algorithm has more obvious woodcut nick texture,and the nick texture distribution is more reasonable;and also the transferred results are more real and natural,which are closer to real woodcut prints.