{"title":"基于glr的自适应卡尔曼滤波去噪","authors":"L. Hong, D. Brzakovic","doi":"10.1109/ICSYSE.1990.203141","DOIUrl":null,"url":null,"abstract":"A method for noise removal in image processing is described. The method does not require any prior knowledge about the image, and it uses a signal model that represents two independent dynamics of the signal. This model is used as the basis for adaptive Kalman filtering. The method is based on the generalised likelihood ratio. It has the capability to retain fast transients that are attributed to important changes in the images while it removes the noise added to the slow transients. The method has been implemented in 1D fashion. With an easy extension, it can be readily implemented in a 2D fashion. Satisfactory results obtained when processing 1D and 2D signals are shown","PeriodicalId":259801,"journal":{"name":"1990 IEEE International Conference on Systems Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"GLR-based adaptive Kalman filtering in noise removal\",\"authors\":\"L. Hong, D. Brzakovic\",\"doi\":\"10.1109/ICSYSE.1990.203141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method for noise removal in image processing is described. The method does not require any prior knowledge about the image, and it uses a signal model that represents two independent dynamics of the signal. This model is used as the basis for adaptive Kalman filtering. The method is based on the generalised likelihood ratio. It has the capability to retain fast transients that are attributed to important changes in the images while it removes the noise added to the slow transients. The method has been implemented in 1D fashion. With an easy extension, it can be readily implemented in a 2D fashion. Satisfactory results obtained when processing 1D and 2D signals are shown\",\"PeriodicalId\":259801,\"journal\":{\"name\":\"1990 IEEE International Conference on Systems Engineering\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1990 IEEE International Conference on Systems Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSYSE.1990.203141\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1990 IEEE International Conference on Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSYSE.1990.203141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GLR-based adaptive Kalman filtering in noise removal
A method for noise removal in image processing is described. The method does not require any prior knowledge about the image, and it uses a signal model that represents two independent dynamics of the signal. This model is used as the basis for adaptive Kalman filtering. The method is based on the generalised likelihood ratio. It has the capability to retain fast transients that are attributed to important changes in the images while it removes the noise added to the slow transients. The method has been implemented in 1D fashion. With an easy extension, it can be readily implemented in a 2D fashion. Satisfactory results obtained when processing 1D and 2D signals are shown
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