TGHop:一个可解释的、高效的、轻量级的纹理生成方法

IF 3.2 Q1 Computer Science
Xuejing Lei, Ganning Zhao, Kaitai Zhang, C. J. Kuo
{"title":"TGHop:一个可解释的、高效的、轻量级的纹理生成方法","authors":"Xuejing Lei, Ganning Zhao, Kaitai Zhang, C. J. Kuo","doi":"10.1017/ATSIP.2021.15","DOIUrl":null,"url":null,"abstract":"An explainable, efficient, and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training. In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate high-quality texture. Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source. Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework. To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples. Finally, texture patches are stitched to form texture images of a large size. It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"TGHop: an explainable, efficient, and lightweight method for texture generation\",\"authors\":\"Xuejing Lei, Ganning Zhao, Kaitai Zhang, C. J. Kuo\",\"doi\":\"10.1017/ATSIP.2021.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An explainable, efficient, and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training. In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate high-quality texture. Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source. Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework. To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples. Finally, texture patches are stitched to form texture images of a large size. It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed.\",\"PeriodicalId\":44812,\"journal\":{\"name\":\"APSIPA Transactions on Signal and Information Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2021-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"APSIPA Transactions on Signal and Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/ATSIP.2021.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"APSIPA Transactions on Signal and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/ATSIP.2021.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 11

摘要

本文提出了一种可解释的、高效的、轻量级的纹理生成方法,称为TGHop (texture generation PixelHop的缩写)。虽然深度神经网络可以合成视觉上令人愉悦的纹理,但相关的模型规模大,难以在理论上解释,并且在训练中计算成本高。相比之下,TGHop模型尺寸小,数学透明,训练和推理效率高,能够生成高质量的纹理。给定一个示例纹理,TGHop首先从中裁剪出许多样本补丁,形成称为源的样本补丁集合。然后,利用PixelHop++框架对来自源的样本进行像素统计分析,得到这些patch的从细到粗的子空间序列。为了使用TGHop生成纹理补丁,我们从最粗糙的子空间(称为核心)开始,并尝试通过遵循真实样本的分布在每个子空间中生成样本。最后,拼接纹理块,形成大尺寸的纹理图像。实验结果表明,TGHop能够以较小的模型尺寸和较快的速度生成质量较好的纹理图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TGHop: an explainable, efficient, and lightweight method for texture generation
An explainable, efficient, and lightweight method for texture generation, called TGHop (an acronym of Texture Generation PixelHop), is proposed in this work. Although synthesis of visually pleasant texture can be achieved by deep neural networks, the associated models are large in size, difficult to explain in theory, and computationally expensive in training. In contrast, TGHop is small in its model size, mathematically transparent, efficient in training and inference, and able to generate high-quality texture. Given an exemplary texture, TGHop first crops many sample patches out of it to form a collection of sample patches called the source. Then, it analyzes pixel statistics of samples from the source and obtains a sequence of fine-to-coarse subspaces for these patches by using the PixelHop++ framework. To generate texture patches with TGHop, we begin with the coarsest subspace, which is called the core, and attempt to generate samples in each subspace by following the distribution of real samples. Finally, texture patches are stitched to form texture images of a large size. It is demonstrated by experimental results that TGHop can generate texture images of superior quality with a small model size and at a fast speed.
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
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信