基于梯度格矩阵描述的图像神经风格迁移

Heng Jin, Tian Wang, Mengyi Zhang, Mingmin Li, Yan Wang, H. Snoussi
{"title":"基于梯度格矩阵描述的图像神经风格迁移","authors":"Heng Jin, Tian Wang, Mengyi Zhang, Mingmin Li, Yan Wang, H. Snoussi","doi":"10.23919/CCC50068.2020.9188652","DOIUrl":null,"url":null,"abstract":"Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Neural Style Transfer for Picture with Gradient Gram Matrix Description\",\"authors\":\"Heng Jin, Tian Wang, Mengyi Zhang, Mingmin Li, Yan Wang, H. Snoussi\",\"doi\":\"10.23919/CCC50068.2020.9188652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.\",\"PeriodicalId\":255872,\"journal\":{\"name\":\"2020 39th Chinese Control Conference (CCC)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 39th Chinese Control Conference (CCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CCC50068.2020.9188652\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 39th Chinese Control Conference (CCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CCC50068.2020.9188652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

尽管神经风格迁移在风格化图片上表现优异,但我们发现Gatys等[1]算法不能完美地重建纹理风格。输出的程式化图像会出现局部浑浊、纹理表达不足等不满意的意外纹理。该方法在原有算法的基础上,增加了对风格损失的梯度图描述,旨在增强纹理表达,消除浑浊。我们的方法在一定程度上延长了运行时间,但其输出的风格化图片在纹理细节上得到了更高的性能,特别是在消除模糊方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Neural Style Transfer for Picture with Gradient Gram Matrix Description
Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Matrix-based Algorithm for the LS Design of Variable Fractional Delay FIR Filters with Constraints MPC Control and Simulation of a Mixed Recovery Dual Channel Closed-Loop Supply Chain with Lead Time Fractional-order ADRC framework for fractional-order parallel systems A Moving Target Tracking Control and Obstacle Avoidance of Quadrotor UAV Based on Sliding Mode Control Using Artificial Potential Field and RBF Neural Networks Finite-time Pinning Synchronization and Parameters Identification of Markovian Switching Complex Delayed Network with Stochastic Perturbations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1