{"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}
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.<>