Outlier robust corner-preserving methods for reconstructing noisy images

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Publicat a:arXiv.org (Aug 3, 2007), p. n/a
Autor principal: Hillebrand, Martin
Altres autors: Müller, Christine H
Publicat:
Cornell University Library, arXiv.org
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Accés en línia:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
024 7 |a 10.1214/009053606000001109  |2 doi 
035 |a 2088010612 
045 0 |b d20070803 
100 1 |a Hillebrand, Martin 
245 1 |a Outlier robust corner-preserving methods for reconstructing noisy images 
260 |b Cornell University Library, arXiv.org  |c Aug 3, 2007 
513 |a Working Paper 
520 3 |a The ability to remove a large amount of noise and the ability to preserve most structure are desirable properties of an image smoother. Unfortunately, they usually seem to be at odds with each other; one can only improve one property at the cost of the other. By combining M-smoothing and least-squares-trimming, the TM-smoother is introduced as a means to unify corner-preserving properties and outlier robustness. To identify edge- and corner-preserving properties, a new theory based on differential geometry is developed. Further, robustness concepts are transferred to image processing. In two examples, the TM-smoother outperforms other corner-preserving smoothers. A software package containing both the TM- and the M-smoother can be downloaded from the Internet. 
653 |a Data analysis 
653 |a Image reconstruction 
653 |a Properties (attributes) 
653 |a Differential geometry 
653 |a Image processing 
653 |a Robustness 
653 |a Preservation 
700 1 |a Müller, Christine H 
773 0 |t arXiv.org  |g (Aug 3, 2007), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2088010612/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/0708.0481