{"title":"高效实现图像恢复算法","authors":"S. Dharanipragada, K. Arun","doi":"10.1109/DSP.1994.379846","DOIUrl":null,"url":null,"abstract":"Presents an efficient implementation scheme for the Newton algorithm for convex set constrained signal recovery [Dharanipragada and Arun, 1993). The implementation avoids matrix creation, inversion and even storage by exploiting the structure of the operators involved and by using conjugate-gradient iterations within each Newton iteration. The resulting algorithm is thus computation and memory efficient and is well-suited for large scale problems typically arising in image recovery applications. The same implementation scheme can also be effectively used for the POCS algorithm.<<ETX>>","PeriodicalId":189083,"journal":{"name":"Proceedings of IEEE 6th Digital Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient implementation of image recovery algorithms\",\"authors\":\"S. Dharanipragada, K. Arun\",\"doi\":\"10.1109/DSP.1994.379846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Presents an efficient implementation scheme for the Newton algorithm for convex set constrained signal recovery [Dharanipragada and Arun, 1993). The implementation avoids matrix creation, inversion and even storage by exploiting the structure of the operators involved and by using conjugate-gradient iterations within each Newton iteration. The resulting algorithm is thus computation and memory efficient and is well-suited for large scale problems typically arising in image recovery applications. The same implementation scheme can also be effectively used for the POCS algorithm.<<ETX>>\",\"PeriodicalId\":189083,\"journal\":{\"name\":\"Proceedings of IEEE 6th Digital Signal Processing Workshop\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE 6th Digital Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSP.1994.379846\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE 6th Digital Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSP.1994.379846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient implementation of image recovery algorithms
Presents an efficient implementation scheme for the Newton algorithm for convex set constrained signal recovery [Dharanipragada and Arun, 1993). The implementation avoids matrix creation, inversion and even storage by exploiting the structure of the operators involved and by using conjugate-gradient iterations within each Newton iteration. The resulting algorithm is thus computation and memory efficient and is well-suited for large scale problems typically arising in image recovery applications. The same implementation scheme can also be effectively used for the POCS algorithm.<>