Image generation of traditional Chinese window grilles based on generative adversarial networks

Chengxi Miao, Jianqin Wu, Jialin Chen, Shiyi Xiong, Lingyue Wang, Qi Wang
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Abstract

Window grille is one of the expressions of traditional Chinese folk arts, which has unique stylistic characteristics and rich symbolic meanings. Studying how to extract the style characteristics of window grilles and generate new window grilles is beneficial to the inheritance and development of traditional Chinese arts. In recent years, the innovative development of generative adversarial networks (GANs) has made it possible to capture the intrinsic distribution of data and generate images that look like real ones. On the basis of researching existing three types of style-based generative adversarial networks (StyleGANs) and adaptive discriminator augmentation (ADA), we use StyleGAN2-ADA to train window grille datasets and generate new window grille images. Finally, multiple image quality evaluation metrics are used to analyze the generated images. The result shows that StyleGAN2-ADA has a good effect on the automatic generation of window grille images. In addition, by comparing the results of different size datasets, we found that the size of dataset has a significant impact on the quality of the generated window grilles.
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基于生成对抗网络的中国传统窗纱图像生成
窗棂是中国传统民间艺术的表现形式之一,具有独特的风格特征和丰富的象征意义。研究如何提取窗棂的风格特征,产生新的窗棂,有利于中国传统艺术的继承和发展。近年来,生成对抗网络(GANs)的创新发展使得捕获数据的内在分布并生成与真实图像相似的图像成为可能。在研究现有的三种基于风格的生成式对抗网络(StyleGANs)和自适应判别器增强(ADA)的基础上,我们使用StyleGAN2-ADA对格栅数据集进行训练并生成新的格栅图像。最后,使用多个图像质量评价指标对生成的图像进行分析。实验结果表明,StyleGAN2-ADA对于自动生成栅格图像具有良好的效果。此外,通过比较不同大小数据集的结果,我们发现数据集的大小对生成的格栅质量有显著的影响。
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