Investigation of Compound Gauss-Markov Image Field
碩士 === 國立中山大學 === 電機工程學系研究所 === 90 === This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image b...
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ndltd-TW-090NSYS54421032015-10-13T12:46:51Z http://ndltd.ncl.edu.tw/handle/69061360610566711775 Investigation of Compound Gauss-Markov Image Field 複合高斯馬可夫影像場之探討 Yan-Li Lin 林炎利 碩士 國立中山大學 電機工程學系研究所 90 This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image based upon the traditional Compound Gauss-Markov image field without the constraint of the model parameters introduced in the original work. The image is restored in two steps iteratively: restoring the line field by the assumed image field and restoring the image field by the just computed line field. Two methods are proposed to replace the traditional method in solving for the line field. They are probability method and vector method. In probability method, we break away from the limitation of the energy function Vcl(L) and the mystical system parameters Ckll(m,n) andσw2. In vector method, the line field appears more reasonable than the original method. The image restored by our methods has a similar visual quality but a better numerical value than the original method. Ben-Shung Chow 周本生 2002 學位論文 ; thesis 61 zh-TW |
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碩士 === 國立中山大學 === 電機工程學系研究所 === 90 ===
This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image based upon the traditional Compound Gauss-Markov image field without the constraint of the model parameters introduced in the original work. The image is restored in two steps iteratively: restoring the line field by the assumed image field and restoring the image field by the just computed line field.
Two methods are proposed to replace the traditional method in solving for the line field. They are probability method and vector method. In probability method, we break away from the limitation of the energy function Vcl(L) and the mystical system parameters Ckll(m,n) andσw2. In vector method, the line field appears more reasonable than the original method. The image restored by our methods has a similar visual quality but a better numerical value than the original method.
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Ben-Shung Chow |
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Ben-Shung Chow Yan-Li Lin 林炎利 |
author |
Yan-Li Lin 林炎利 |
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Yan-Li Lin 林炎利 Investigation of Compound Gauss-Markov Image Field |
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Yan-Li Lin |
title |
Investigation of Compound Gauss-Markov Image Field |
title_short |
Investigation of Compound Gauss-Markov Image Field |
title_full |
Investigation of Compound Gauss-Markov Image Field |
title_fullStr |
Investigation of Compound Gauss-Markov Image Field |
title_full_unstemmed |
Investigation of Compound Gauss-Markov Image Field |
title_sort |
investigation of compound gauss-markov image field |
publishDate |
2002 |
url |
http://ndltd.ncl.edu.tw/handle/69061360610566711775 |
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