{"title":"从素描到中国画通过增强素描的三阶段渐进生成网络","authors":"","doi":"10.1016/j.jfranklin.2024.107246","DOIUrl":null,"url":null,"abstract":"<div><p>With the proposal and wide application of Generative Adversarial Networks (GAN), sketch-based image generation has gradually become a research hotspot. Because of its unique artistic characteristics, Chinese painting has attracted more and more people to engage in research in the field of sketch-based Chinese painting. Most existing researches on sketch generation of Chinese paintings tend to extract edge maps from mature Chinese paintings and train generative models. When edge maps are extracted from sketches with sparse lines as input for generation, the quality of the generated Chinese painting is poor. This paper proposes a three-stage progressive Chinese painting generation network based on sketch. By the reduction and enhancement networks, our model converts the input sketch into types of sketches with different line richness. Each stage is used to learn to generate different Chinese painting information, realizing the progressive generation of Chinese painting through three connected generation networks. The experimental results show that our model can generate better-quality Chinese paintings and perform better in generating Chinese paintings from sketches.</p></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sketch to Chinese paintings: A three-stage progressive generation network via enhancing sketch\",\"authors\":\"\",\"doi\":\"10.1016/j.jfranklin.2024.107246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the proposal and wide application of Generative Adversarial Networks (GAN), sketch-based image generation has gradually become a research hotspot. Because of its unique artistic characteristics, Chinese painting has attracted more and more people to engage in research in the field of sketch-based Chinese painting. Most existing researches on sketch generation of Chinese paintings tend to extract edge maps from mature Chinese paintings and train generative models. When edge maps are extracted from sketches with sparse lines as input for generation, the quality of the generated Chinese painting is poor. This paper proposes a three-stage progressive Chinese painting generation network based on sketch. By the reduction and enhancement networks, our model converts the input sketch into types of sketches with different line richness. Each stage is used to learn to generate different Chinese painting information, realizing the progressive generation of Chinese painting through three connected generation networks. The experimental results show that our model can generate better-quality Chinese paintings and perform better in generating Chinese paintings from sketches.</p></div>\",\"PeriodicalId\":17283,\"journal\":{\"name\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Franklin Institute-engineering and Applied Mathematics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0016003224006677\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006677","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Sketch to Chinese paintings: A three-stage progressive generation network via enhancing sketch
With the proposal and wide application of Generative Adversarial Networks (GAN), sketch-based image generation has gradually become a research hotspot. Because of its unique artistic characteristics, Chinese painting has attracted more and more people to engage in research in the field of sketch-based Chinese painting. Most existing researches on sketch generation of Chinese paintings tend to extract edge maps from mature Chinese paintings and train generative models. When edge maps are extracted from sketches with sparse lines as input for generation, the quality of the generated Chinese painting is poor. This paper proposes a three-stage progressive Chinese painting generation network based on sketch. By the reduction and enhancement networks, our model converts the input sketch into types of sketches with different line richness. Each stage is used to learn to generate different Chinese painting information, realizing the progressive generation of Chinese painting through three connected generation networks. The experimental results show that our model can generate better-quality Chinese paintings and perform better in generating Chinese paintings from sketches.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.