CBA-GAN: Cartoonization style transformation based on the convolutional attention module

Zhang, Feng, Zhao, Huihuang, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Wu, Yichun and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 2023. CBA-GAN: Cartoonization style transformation based on the convolutional attention module. Computers and Electrical Engineering 106 , 108575. 10.1016/j.compeleceng.2022.108575

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Abstract

Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0045-7906
Date of First Compliant Deposit:	26 January 2023
Date of Acceptance:	31 December 2022
Last Modified:	13 Jan 2024 02:30
URI:	https://orca.cardiff.ac.uk/id/eprint/156269

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