多通道图像的数字最小二乘恢复

N. P. Galatsanos, R. Chin
{"title":"多通道图像的数字最小二乘恢复","authors":"N. P. Galatsanos, R. Chin","doi":"10.1109/MDSP.1989.97105","DOIUrl":null,"url":null,"abstract":"Summary form only given. A least-squares filter for the restoration of multichannel images is presented. The process involves the removal of noise and degradation from observed multichannel imagery, such as color or multispectral images. The restoration filters utilize information distributed across image channels and process all channels as a single entity. They use a priori information and constraints, thus avoiding some of the drawbacks of the minimum-mean-squared-error filter.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital least squares restoration of multi-channel images\",\"authors\":\"N. P. Galatsanos, R. Chin\",\"doi\":\"10.1109/MDSP.1989.97105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. A least-squares filter for the restoration of multichannel images is presented. The process involves the removal of noise and degradation from observed multichannel imagery, such as color or multispectral images. The restoration filters utilize information distributed across image channels and process all channels as a single entity. They use a priori information and constraints, thus avoiding some of the drawbacks of the minimum-mean-squared-error filter.<<ETX>>\",\"PeriodicalId\":340681,\"journal\":{\"name\":\"Sixth Multidimensional Signal Processing Workshop,\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth Multidimensional Signal Processing Workshop,\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MDSP.1989.97105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth Multidimensional Signal Processing Workshop,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDSP.1989.97105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

只提供摘要形式。提出了一种用于多通道图像恢复的最小二乘滤波器。该过程包括从观察到的多通道图像(如彩色或多光谱图像)中去除噪声和退化。恢复过滤器利用分布在图像通道上的信息,并将所有通道作为单个实体处理。它们使用先验信息和约束,从而避免了最小均方误差过滤器的一些缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital least squares restoration of multi-channel images
Summary form only given. A least-squares filter for the restoration of multichannel images is presented. The process involves the removal of noise and degradation from observed multichannel imagery, such as color or multispectral images. The restoration filters utilize information distributed across image channels and process all channels as a single entity. They use a priori information and constraints, thus avoiding some of the drawbacks of the minimum-mean-squared-error filter.<>
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