Transforming photos to comics using convolutional neural networks

Chen, Yang, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Liu, Yong-Jin 2017. Transforming photos to comics using convolutional neural networks. Presented at: IEEE International Conference on Image Processing (ICIP), Beijing, China, 17-20 Sep 2017.

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Abstract

In this paper, inspired by Gatys’s recent work, we propose a novel approach that transforms photos to comics using deep convolutional neural networks (CNNs). While Gatys’s method that uses a pre-trained VGG network generally works well for transferring artistic styles such as painting from a style image to a content image, for more minimalist styles such as comics, the method often fails to produce satisfactory results. To address this, we further introduce a dedicated comic style CNN, which is trained for classifying comic images and photos. This new network is effective in capturing various comic styles and thus helps to produce better comic stylization results. Even with a grayscale style image, Gatys’s method can still produce colored output, which is not desirable for comics. We develop a modified optimization framework such that a grayscale image is guaranteed to be synthesized. To avoid converging to poor local minima, we further initialize the output image using grayscale version of the content image. Various examples show that our method synthesizes better comic images than the state-of-the-art method.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Funders:	Royal Society-Newton
Related URLs:	Organisation
Date of First Compliant Deposit:	28 May 2017
Last Modified:	21 Oct 2022 07:52
URI:	https://orca.cardiff.ac.uk/id/eprint/100937

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