Journal of Applied Science and Engineering

Published by Tamkang University Press


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Guangpeng Yue1, Mingxi Li2, and Hongyan Zheng3This email address is being protected from spambots. You need JavaScript enabled to view it.

1School of Industrial Design, Lu Xun Academy of Fine Arts, Shenyang 110000 China

2School of Media Animation, Lu Xun Academy of Fine Arts, Dalian 116000 China

3Basic Teaching Department, Lu Xun Academy of Fine Arts, Dalian 116000 China



Received: February 16, 2023
Accepted: March 19, 2024
Publication Date: April 13, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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With the rapid development of deep learning, image style transfer has become one of the research hotspots in the field of computer vision. However, there are some problems in the style transfer model, such as fuzzy image details, poor style texture and color effect, and too many model parameters. In this paper, a novel style transfer method based on Generative Adversarial network and feature transformation is proposed. By adding Ghost convolutional module and anti-residual improved module to optimize the generator network structure, this process can reduce the number of model parameters and calculation costs, and enhance the feature extraction ability of the network. Self-attention mechanism is introduced to obtain more image features and improve the generation quality of generator. Content style loss, color reconstruction loss and mapping consistency loss are added to the loss function to improve the generation ability of the model and improve the quality of the generated image. Experimental results on data sets show that the PSNR and SSIM values of images generated by this method are higher than those of comparison methods.

Keywords: deep learning, image style transfer, Generative Adversarial Network, feature transformation, Self-attention mechanism

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