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Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation
Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Japan.ORCID iD: 0000-0002-4255-5130
2015 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 11, 1950-1954 p.Article in journal (Refereed) Published
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Abstract [en]

Filtering additive white Gaussian noise in images using the best linear unbiased estimator (BLUE) is technically sound in a sense that it is an optimal average filter derived from the statistical estimation theory. The BLUE filter mask has the theoretical advantage in that its shape and its size are formulated in terms of the image signals and associated noise components. However, like many other noise filtering problems, prior knowledge about the additive noise needs to be available, which is often obtained using training data. This paper presents the sequential Gaussian simulation in geostatistics for measuring signal and noise variances in images without the need of training data for the BLUE filter implementation. The simulated signal variance and the BLUE average can be further used as parameters of the adaptive Wiener filter for image restoration.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. Vol. 22, no 11, 1950-1954 p.
Keyword [en]
Adaptive Wiener filter, best linear unbiased estimator, image restoration, kriging, optimal average filter, sequential Gaussian simulation
National Category
Medical Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-128596DOI: 10.1109/LSP.2015.2448732ISI: 000357620000001Scopus ID: 2-s2.0-84936805177OAI: oai:DiVA.org:liu-128596DiVA: diva2:930714
Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2017-12-07Bibliographically approved

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Pham, Tuan D

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  • apa
  • harvard1
  • ieee
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Language
  • de-DE
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