{"title":"用于图像恢复的自举均值滤波器","authors":"C. Lam","doi":"10.1109/ACSSC.1993.342582","DOIUrl":null,"url":null,"abstract":"A bootstrap mean is calculated as follows. An artificial data set is generated by randomly sampling the original data set. A trimmed mean is then calculated for each of the artificial data set. These steps are repeated many times to produce a set of trimmed means. The bootstrap mean is the average of these trimmed means. The bootstrap mean is a more robust estimate of the true mean and the estimation of error is the usual standard deviation. Recent advances in fast computers make it feasible to calculate the bootstrap mean. A bootstrap mean filter was developed and tested using synthetic data with random noise added. Comparisons to mean, median, and trimmed-mean filters show that the bootstrap mean filter is superior in the removal of random noise and the retention of edge information. Implementation in special purpose hardware of this filter is desirable because of its heavy computational requirement. Some candidate solutions are suggested.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The bootstrap mean filter for image restoration\",\"authors\":\"C. Lam\",\"doi\":\"10.1109/ACSSC.1993.342582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A bootstrap mean is calculated as follows. An artificial data set is generated by randomly sampling the original data set. A trimmed mean is then calculated for each of the artificial data set. These steps are repeated many times to produce a set of trimmed means. The bootstrap mean is the average of these trimmed means. The bootstrap mean is a more robust estimate of the true mean and the estimation of error is the usual standard deviation. Recent advances in fast computers make it feasible to calculate the bootstrap mean. A bootstrap mean filter was developed and tested using synthetic data with random noise added. Comparisons to mean, median, and trimmed-mean filters show that the bootstrap mean filter is superior in the removal of random noise and the retention of edge information. Implementation in special purpose hardware of this filter is desirable because of its heavy computational requirement. Some candidate solutions are suggested.<<ETX>>\",\"PeriodicalId\":266447,\"journal\":{\"name\":\"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.1993.342582\",\"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 27th Asilomar Conference on Signals, Systems and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1993.342582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A bootstrap mean is calculated as follows. An artificial data set is generated by randomly sampling the original data set. A trimmed mean is then calculated for each of the artificial data set. These steps are repeated many times to produce a set of trimmed means. The bootstrap mean is the average of these trimmed means. The bootstrap mean is a more robust estimate of the true mean and the estimation of error is the usual standard deviation. Recent advances in fast computers make it feasible to calculate the bootstrap mean. A bootstrap mean filter was developed and tested using synthetic data with random noise added. Comparisons to mean, median, and trimmed-mean filters show that the bootstrap mean filter is superior in the removal of random noise and the retention of edge information. Implementation in special purpose hardware of this filter is desirable because of its heavy computational requirement. Some candidate solutions are suggested.<>
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