REN Jianjun, ZHANG Weizheng, ZHANG Weiwei, et al. Research on the transfer of artistic style of blue-and-white porcelain decoration[J]. Journal of Light Industry, 2022, 37(5): 113-119,126. doi: 10.12187/2022.05.014
Citation:
REN Jianjun, ZHANG Weizheng, ZHANG Weiwei, et al. Research on the transfer of artistic style of blue-and-white porcelain decoration[J]. Journal of Light Industry, 2022, 37(5): 113-119,126.
doi:
10.12187/2022.05.014
Research on the transfer of artistic style of blue-and-white porcelain decoration
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1. College of Arts and Design, Zhengzhou University of Light Industry, Zhengzhou 450002, China;
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2. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China
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Received Date:
2021-07-29
Accepted Date:
2022-03-26
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Abstract
Modern antiques and Internet + were the development trends of high-quality ceramics. It was of great significance to introduce the artistic style of ceramic decoration into contemporary products and realize the fusion of time and space and artistic re-creation. Taking blue-and-white porcelain as the research object, the near-modern landscape painting was used as the content to superimpose random Gaussian white noise to obtain the initial style transfer image. The pre-trained convolutional neural network was used to extract the content image and the content feature of the generated image, and the style image of the porcelain decoration and the style feature of the generated image were extracted. The content loss and style loss were calculated separately. By the calculation of the loss function and iterating to minimize it, a style transfer image with a strong blue-and-white porcelain flavor was finally generated. The experimental results showed that the deep learning method could realize the integration of blue-andwhite porcelain decoration and landscape paintings, and successfully transfer the learned characteristics of blueand-white porcelain decoration to landscape paintings, which not only expanded the application scope of image style transfer, but also produced a new visual experience, with a certain artistic appreciation and innovation.
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References
-
[1]
招伟培,罗宏,龙海仁,等.浅谈陕西铜川原材料及仿古、全抛釉瓷砖的坯体配方设计[J].佛山陶瓷,2020,30(7):31-36.
-
[2]
陈新,汪洲.现代仿古瓷的价值[J].景德镇陶瓷,2012,23(4):32.
-
[3]
赵国学.瓷话传统与现代[J].景德镇陶瓷,2019(6):12-13.
-
[4]
何笠农.传承与创新的当代陶瓷艺术[J].中国陶瓷,2012,48(2):62-65.
-
[5]
杜斐,陈明非,张云港.背景保持的服装片段风格迁移[J].软件导刊,2021,20(3):214-219.
-
[6]
邓筱,徐梦秋,吴铭,等.基于语义分割的织锦类自适应风格迁移算法[J].北京邮电大学学报,2021,44(1):117-123.
-
[7]
潘今一,王亚蒙,王伟,等.基于风格迁移和薄板样条的扩充汉字样本方法[J].浙江工业大学学报,2020,48(1):25-29
,39.
-
[8]
李进,高静,陈俊杰,等.基于条件生成对抗网络的蒙古文字体风格迁移模型[J].中文信息学报,2020,34(4):58-62
,71.
-
[9]
陈捷,徐戈.风格迁移算法在漆艺文创品设计中的运用[J].装饰,2020(3):82-85.
-
[10]
董荪,丁友东,钱昀.基于人工智能的风格迁移算法在动画特效设计中的应用[J].装饰,2018(1):104-107.
-
[11]
侯宇康,吕健,刘翔,等.基于神经风格迁移网络的民族图案创新方法[J].图学学报, 2020,41(4):606-613.
-
[12]
金思雨,覃京燕.基于计算机图像风格迁移的音乐可视化智能设计研究[J].包装工程,2020,41(16):193-198.
-
[13]
SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Internation Conference on Learning Representation 2015. America California:University of California Press,2015:1-14.
-
[14]
RUSSAKOVSKY O,DENG J,SU H, et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
-
[15]
张月,刘彩云,熊杰.基于VGG-19图像风格迁移算法的设计与分析[J].信息技术与信息化,2020(1):70-72.
-
[16]
LI Y H,WANG N Y,LIU J Y,et al.Demystifying neural style transfer[C]//ACM.Proceedings of the 26th International Joint Conference on Artificial Intelligence.Australia Melbourne:AAAI Press,2017:2230-2236.
-
[17]
牟晋娟.基于深度学习的图像风格迁移技术的研究[J].电子元器件与信息技术,2019,22(4):86-89.
-
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