Open Access
ARTICLE
Implementation of Art Pictures Style Conversion with GAN
Xinlong Wu1, Desheng Zheng1,*, Kexin Zhang1, Yanling Lai1, Zhifeng Liu1, Zhihong Zhang2
1 School of Computer Science, Southwest Petroleum University, Chengdu, 610000, China
2 AECC Sichuan Gas Turbine Establishment, Mianyang, 621700, China
* Corresponding Author: Desheng Zheng. Email:
Journal of Quantum Computing 2021, 3(4), 127-136. https://doi.org/10.32604/jqc.2021.017251
Received 20 July 2021; Accepted 21 September 2021; Issue published 10 January 2022
Abstract
Image conversion refers to converting an image from one style to
another and ensuring that the content of the image remains unchanged. Using
Generative Adversarial Networks (GAN) for image conversion can achieve good
results. However, if there are enough samples, any image in the target domain can
be mapped to the same set of inputs. On this basis, the Cycle Consistency
Generative Adversarial Network (CycleGAN) was developed. This article verifies
and discusses the advantages and disadvantages of the CycleGAN model in image
style conversion. CycleGAN uses two generator networks and two discriminator
networks. The purpose is to learn the mapping relationship and inverse mapping
relationship between the source domain and the target domain. It can reduce the
mapping and improve the quality of the generated image. Through the idea of loop,
the loss of information in image style conversion is reduced. When evaluating the
results of the experiment, the degree of retention of the input image content will
be judged. Through the experimental results, CycleGAN can understand the
artist’s overall artistic style and successfully convert real landscape paintings. The
advantage is that most of the content of the original picture can be retained, and
only the texture line of the picture is changed to a level similar to the artist’s style.
Keywords
Cite This Article
X. Wu, D. Zheng, K. Zhang, Y. Lai, Z. Liu
et al., "Implementation of art pictures style conversion with gan,"
Journal of Quantum Computing, vol. 3, no.4, pp. 127–136, 2021. https://doi.org/10.32604/jqc.2021.017251