Digital image restoration using autoregressive time series type models. 1998. abstract bibtex We consider a non-symmetric half plane autoregressive image, where the image intensity of a point is a linear combination of the intensitites of the eight nearest points located on one quadrant of the coordinate plane, plus a normal white noise innovations process. Two types of contaminations are considered. Innovation outliers, where a fraction of innovations are corrupted with a heavy tailed outlier generatioin process, and additive outliers, where a fraction of observations are corrupted. We develop a GM-estimator for the robust estimation of parameters of a contamined autoregressive image model, based on time series GM-estimators introduced by Denby and Martin (1979) applied to the restoration of radar generated images. Ordinary least-squares estimators are asymptotically efficient with a non- contamined gaussian process, like the one considered here. M-estimators behave better when innovation outliers are present, but are very sensitive to additive outliers. A simulation study is carried out, which shows that the GM-estimator introduced here has a better performance with an additive outlier contamined image model than M-estimators and ordinary least squares estimators.
@book{0032461523,
abstract = "We consider a non-symmetric half plane autoregressive image, where the image intensity of a point is a linear combination of the intensitites of the eight nearest points located on one quadrant of the coordinate plane, plus a normal white noise innovations process. Two types of contaminations are considered. Innovation outliers, where a fraction of innovations are corrupted with a heavy tailed outlier generatioin process, and additive outliers, where a fraction of observations are corrupted. We develop a GM-estimator for the robust estimation of parameters of a contamined autoregressive image model, based on time series GM-estimators introduced by Denby and Martin (1979) applied to the restoration of radar generated images. Ordinary least-squares estimators are asymptotically efficient with a non- contamined gaussian process, like the one considered here. M-estimators behave better when innovation outliers are present, but are very sensitive to additive outliers. A simulation study is carried out, which shows that the GM-estimator introduced here has a better performance with an additive outlier contamined image model than M-estimators and ordinary least squares estimators.",
year = "1998",
title = "Digital image restoration using autoregressive time series type models",
pages = "53-59",
booktitle = "Second Latino-American seminar on radar remote sensing image processing techniques"
}
Downloads: 0
{"_id":"o6kv3AGkTgAEM2JYF","bibbaseid":"anonymous-digitalimagerestorationusingautoregressivetimeseriestypemodels-1998","downloads":0,"creationDate":"2017-03-31T20:15:32.601Z","title":"Digital image restoration using autoregressive time series type models","author_short":null,"year":1998,"bibtype":"book","biburl":"https://1fichier.com/?j9cpurkmnv","bibdata":{"bibtype":"book","type":"book","abstract":"We consider a non-symmetric half plane autoregressive image, where the image intensity of a point is a linear combination of the intensitites of the eight nearest points located on one quadrant of the coordinate plane, plus a normal white noise innovations process. Two types of contaminations are considered. Innovation outliers, where a fraction of innovations are corrupted with a heavy tailed outlier generatioin process, and additive outliers, where a fraction of observations are corrupted. We develop a GM-estimator for the robust estimation of parameters of a contamined autoregressive image model, based on time series GM-estimators introduced by Denby and Martin (1979) applied to the restoration of radar generated images. Ordinary least-squares estimators are asymptotically efficient with a non- contamined gaussian process, like the one considered here. M-estimators behave better when innovation outliers are present, but are very sensitive to additive outliers. A simulation study is carried out, which shows that the GM-estimator introduced here has a better performance with an additive outlier contamined image model than M-estimators and ordinary least squares estimators.","year":"1998","title":"Digital image restoration using autoregressive time series type models","pages":"53-59","booktitle":"Second Latino-American seminar on radar remote sensing image processing techniques","bibtex":"@book{0032461523,\n abstract = \"We consider a non-symmetric half plane autoregressive image, where the image intensity of a point is a linear combination of the intensitites of the eight nearest points located on one quadrant of the coordinate plane, plus a normal white noise innovations process. Two types of contaminations are considered. Innovation outliers, where a fraction of innovations are corrupted with a heavy tailed outlier generatioin process, and additive outliers, where a fraction of observations are corrupted. We develop a GM-estimator for the robust estimation of parameters of a contamined autoregressive image model, based on time series GM-estimators introduced by Denby and Martin (1979) applied to the restoration of radar generated images. Ordinary least-squares estimators are asymptotically efficient with a non- contamined gaussian process, like the one considered here. M-estimators behave better when innovation outliers are present, but are very sensitive to additive outliers. A simulation study is carried out, which shows that the GM-estimator introduced here has a better performance with an additive outlier contamined image model than M-estimators and ordinary least squares estimators.\",\n year = \"1998\",\n title = \"Digital image restoration using autoregressive time series type models\",\n pages = \"53-59\",\n booktitle = \"Second Latino-American seminar on radar remote sensing image processing techniques\"\n}\n\n","key":"0032461523","id":"0032461523","bibbaseid":"anonymous-digitalimagerestorationusingautoregressivetimeseriestypemodels-1998","urls":{},"downloads":0,"html":""},"search_terms":["digital","image","restoration","using","autoregressive","time","series","type","models"],"keywords":[],"authorIDs":[],"dataSources":["gKiCRHjjC2iGthGEx"]}