Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

<Previous Article In Volume
Next Article In Volume>

Comic Image Style Transfer Based on De-GAN

Authors
Zuoyun Yang1, Hongqiong Huang1, *
1School of Information Engineering, Shanghai Maritime University, Shanghai, China
*Corresponding author. Email: hqhuang@shmtu.edu.cn
Corresponding Author
Hongqiong Huang
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_99How to use a DOI?
Keywords
comic style transfer; generative adversarial network; color reconstruction; feature extraction; image conversion
Abstract

There are still many problems in the current comic style transfer method, such as the style of the generated image does not conform to people's aesthetics, the color is far from the original image, and so on. This paper proposes a new network architecture based on the idea of generative adversarial networks. For the generator, the Desnet module is introduced in the feature conversion layer, which reduces the amount of network parameters while optimizing the efficiency of feature extraction. For the discriminator, this paper introduces layer normalization to denoise the image to solve the problem of image artifacts. In terms of loss function, this paper introduces the color reconstruction loss item to supplement the original loss function, which improves the color of the generated comic image and makes it closer to the original painting. The experimental results show that compared with the current mainstream generative adversarial network, the network model in this paper has achieved better results in the field of comic style transfer.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_99
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_99How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zuoyun Yang
AU  - Hongqiong Huang
PY  - 2023
DA  - 2023/08/10
TI  - Comic Image Style Transfer Based on De-GAN
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 949
EP  - 956
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_99
DO  - 10.2991/978-94-6463-198-2_99
ID  - Yang2023
ER  -