{"title":"基于高阶统计量的图像模糊识别","authors":"Xu You, G. Crebbin","doi":"10.1109/ICIP.1996.560373","DOIUrl":null,"url":null,"abstract":"In this paper, higher order statistic (HOS) based blur identification methods are proposed to estimate blur coefficients in image restoration, in which the image is considered as a colored process. One dimensional (1-D) based blur identification algorithms are proposed, and their extensions to two dimensional (2-D) cases are discussed. The experimental results are presented to demonstrate the performance of the proposed methods in this paper.","PeriodicalId":192947,"journal":{"name":"Proceedings of 3rd IEEE International Conference on Image Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Image blur identification by using higher order statistic techniques\",\"authors\":\"Xu You, G. Crebbin\",\"doi\":\"10.1109/ICIP.1996.560373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, higher order statistic (HOS) based blur identification methods are proposed to estimate blur coefficients in image restoration, in which the image is considered as a colored process. One dimensional (1-D) based blur identification algorithms are proposed, and their extensions to two dimensional (2-D) cases are discussed. The experimental results are presented to demonstrate the performance of the proposed methods in this paper.\",\"PeriodicalId\":192947,\"journal\":{\"name\":\"Proceedings of 3rd IEEE International Conference on Image Processing\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 3rd IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.1996.560373\",\"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 3rd IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.1996.560373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image blur identification by using higher order statistic techniques
In this paper, higher order statistic (HOS) based blur identification methods are proposed to estimate blur coefficients in image restoration, in which the image is considered as a colored process. One dimensional (1-D) based blur identification algorithms are proposed, and their extensions to two dimensional (2-D) cases are discussed. The experimental results are presented to demonstrate the performance of the proposed methods in this paper.