{"title":"基于吉布斯分布的马尔可夫随机场模型的噪声图像分割","authors":"M. El-Gabali, M. Shridhar, M. Ahmadi","doi":"10.1109/ICASSP.1987.1169695","DOIUrl":null,"url":null,"abstract":"Restoration of noisy images through the use of a Gibbs field model for the image and a Gaussian random field characterization of the corrupting noise is discussed in this paper. Two new algorithms capable of parallel implementation are presented and shown to yield satisfactory restoration of images corrupted by high levels of noise.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Segmentation of noisy images modelled by Markov random fields with Gibbs distribution\",\"authors\":\"M. El-Gabali, M. Shridhar, M. Ahmadi\",\"doi\":\"10.1109/ICASSP.1987.1169695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Restoration of noisy images through the use of a Gibbs field model for the image and a Gaussian random field characterization of the corrupting noise is discussed in this paper. Two new algorithms capable of parallel implementation are presented and shown to yield satisfactory restoration of images corrupted by high levels of noise.\",\"PeriodicalId\":140810,\"journal\":{\"name\":\"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1987-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.1987.1169695\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1987.1169695","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Segmentation of noisy images modelled by Markov random fields with Gibbs distribution
Restoration of noisy images through the use of a Gibbs field model for the image and a Gaussian random field characterization of the corrupting noise is discussed in this paper. Two new algorithms capable of parallel implementation are presented and shown to yield satisfactory restoration of images corrupted by high levels of noise.
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