图像锐化使用矩阵Riesz分数阶微分和离散正弦变换

Su-Ling Lee, C. Tseng
{"title":"图像锐化使用矩阵Riesz分数阶微分和离散正弦变换","authors":"Su-Ling Lee, C. Tseng","doi":"10.1109/ICCE-TW.2016.7520915","DOIUrl":null,"url":null,"abstract":"In this paper, the design of matrix Riesz fractional order differentiator (FOD) using discrete sine transform (DST) is presented. First, the matrix Riesz FOD design problem is described. Then, the transfer matrix of the matrix Riesz FOD is obtained by using DST. Next, the designed matrix Riesz FOD is applied to develop an image sharpening algorithm. Finally, an example is presented to show the usefulness of the proposed DST-based matrix Riesz FOD method.","PeriodicalId":6620,"journal":{"name":"2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"7 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Image sharpening using matrix Riesz fractional order differentiator and discrete sine transform\",\"authors\":\"Su-Ling Lee, C. Tseng\",\"doi\":\"10.1109/ICCE-TW.2016.7520915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the design of matrix Riesz fractional order differentiator (FOD) using discrete sine transform (DST) is presented. First, the matrix Riesz FOD design problem is described. Then, the transfer matrix of the matrix Riesz FOD is obtained by using DST. Next, the designed matrix Riesz FOD is applied to develop an image sharpening algorithm. Finally, an example is presented to show the usefulness of the proposed DST-based matrix Riesz FOD method.\",\"PeriodicalId\":6620,\"journal\":{\"name\":\"2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)\",\"volume\":\"7 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-TW.2016.7520915\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-TW.2016.7520915","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

提出了利用离散正弦变换(DST)设计矩阵Riesz分数阶微分器(FOD)。首先,描述了矩阵Riesz FOD设计问题。然后,利用DST得到矩阵Riesz FOD的传递矩阵。然后,利用所设计的矩阵Riesz FOD开发图像锐化算法。最后,给出了一个算例,说明了所提出的基于dst的矩阵Riesz FOD方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image sharpening using matrix Riesz fractional order differentiator and discrete sine transform
In this paper, the design of matrix Riesz fractional order differentiator (FOD) using discrete sine transform (DST) is presented. First, the matrix Riesz FOD design problem is described. Then, the transfer matrix of the matrix Riesz FOD is obtained by using DST. Next, the designed matrix Riesz FOD is applied to develop an image sharpening algorithm. Finally, an example is presented to show the usefulness of the proposed DST-based matrix Riesz FOD method.
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
×
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