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A novel local denoising scheme based on context
Liao Z.W.1; Hu S.X.2; Tang Y.Y.3
2005-12-12
Conference Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Source PublicationProceedings of the Fourth International Conference on Machine Learning and Cybernetics
Pages5496-5500
Conference Date18-21 Aug. 2005
Conference PlaceGuangzhou, China
CountryChina
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

There are two types of traditional denoising methods: one is neighborhood method; the other is contextual method. Recently, some hybrids are proposed and reported good denoising results. However, the basic idea about these hybrids is the parameter of the image is estimated in a set of moving windows with the context, which leads to high complexity to the algorithms. Besides this, some moving windows cannot ensure the numbers of points that have the same context are large enough to obtain reliable estimated parameters. In this paper, we proposed a novel denoising scheme, which can adjust the sizes of local windows automatically according to the numbers of the contextual points. The division of the same contextual points is obtained by dividing the subband into four equal squares if the number of the points is in a suitable extension. Then the division can be done step by step until the number of the points is not in the extension. All divisions can be obtained according to these steps. The other assumption about our framework is the parameter in the same local window is same. Therefore, we can share statistical information among these pixels. Based on these assumptions, we propose a simple example to demonstrate the power of our new scheme. The experimental results show that the new framework improves the denoising results greatly even using the simplest model. 

KeywordContext Image Denoising Local Denoising Method Mmse Psnr Wavelet
DOIhttps://doi.org/10.1109/ICMLC.2005.1527915
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000235325608046
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.School of Applied Mathematics, University of Electronic Science and Technology of China
2.School of Physical Electronics, University of Electronic Science and Technology of China
3.Department of Computer Science, Hong Kong Baptist University, Kowlong Tong, Hong Kong
Recommended Citation
GB/T 7714
Liao Z.W.,Hu S.X.,Tang Y.Y.. A novel local denoising scheme based on context[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2005:5496-5500.
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