Research on Image Sketch Style Transfer Based on GAN Algorithm
WANG Jun
QU Shendi
CHENG Yong
Abstract：The paper aims to solve the problems of weakened image spatial information,the low-quality local structure of the output image,and the inapplicability to the research of sketch style transfer(SST)of traditional generative adversarial network(GAN).Firstly,the key technologies adopted here are elaborated and analyzed in detail.Secondly,the convolutional neural network(CNN)with excellent feature extraction ability is introduced into GAN to construct an SST model.Finally,the validity of this model is verified by constructing data sets and designing experiments.The experimental results show that the artificial subjective score ob-tained by the SST model is 25.59%higher than other algorithms.When the index value calculation is used for objective evaluation,the overall quality of images output by the SST model on different data sets is much higher than that of other algorithms.The SST model reported here can still generate sketch-style images with high similarity to the original input images of non-frontal portrait that do not belong to the training set.Besides,each index is better than that of other algorithms.To sum up,the SST model proposed here has excellent image generation ability and broad application prospects in the study of SST.The purpose of the present work is to provide important technical support for the improvement of image SST technology,as well as a reference for the research field of face identification.
Keywords：GANCNNSST modelsketch style transfer
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3234-3239,3269 )