Statistically based multiwavelet denoising
Saved in:
| Title: | Statistically based multiwavelet denoising |
|---|---|
| Authors: | Bacchelli, S.1 silvia@csr.unibo.it, Papi, S. spapi@csr.unibo.it |
| Source: | Journal of Computational & Applied Mathematics. Dec2007, Vol. 210 Issue 1/2, p47-55. 9p. |
| Subjects: | Wavelets (Mathematics), Harmonic analysis (Mathematics), Image analysis, Fractional parentage coefficients |
| Abstract: | Abstract: In this work, we consider a statistically based multiwavelet thresholding method which acts on the empirical wavelet coefficients in groups, rather than individually, in order to obtain an edge-preserving image denoising technique. Our strategy allows us to exploit the dependencies between neighboring coefficients to make a simultaneous thresholding decision, so that estimation accuracy is increased. By interpreting the multiwavelet analysis in a statistical context, we propose a new weighted multiwavelet matrix thresholding rule, based on the statistical modeling of empirical coefficients. This allows the thresholding decision to be adapted to the local structure of the underlying image, hence producing edge-preserving denoising. Extensive numerical results are presented showing the performance of our denoising procedure. [Copyright &y& Elsevier] |
| Copyright of Journal of Computational & Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 26995198 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Statistically based multiwavelet denoising – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bacchelli%2C+S%2E%22">Bacchelli, S.</searchLink><relatesTo>1</relatesTo><i> silvia@csr.unibo.it</i><br /><searchLink fieldCode="AR" term="%22Papi%2C+S%2E%22">Papi, S.</searchLink><i> spapi@csr.unibo.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+%26+Applied+Mathematics%22">Journal of Computational & Applied Mathematics</searchLink>. Dec2007, Vol. 210 Issue 1/2, p47-55. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Harmonic+analysis+%28Mathematics%29%22">Harmonic analysis (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Fractional+parentage+coefficients%22">Fractional parentage coefficients</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: In this work, we consider a statistically based multiwavelet thresholding method which acts on the empirical wavelet coefficients in groups, rather than individually, in order to obtain an edge-preserving image denoising technique. Our strategy allows us to exploit the dependencies between neighboring coefficients to make a simultaneous thresholding decision, so that estimation accuracy is increased. By interpreting the multiwavelet analysis in a statistical context, we propose a new weighted multiwavelet matrix thresholding rule, based on the statistical modeling of empirical coefficients. This allows the thresholding decision to be adapted to the local structure of the underlying image, hence producing edge-preserving denoising. Extensive numerical results are presented showing the performance of our denoising procedure. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computational & Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=26995198 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.cam.2006.10.091 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 47 Subjects: – SubjectFull: Wavelets (Mathematics) Type: general – SubjectFull: Harmonic analysis (Mathematics) Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Fractional parentage coefficients Type: general Titles: – TitleFull: Statistically based multiwavelet denoising Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bacchelli, S. – PersonEntity: Name: NameFull: Papi, S. IsPartOfRelationships: – BibEntity: Dates: – D: 31 M: 12 Text: Dec2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 03770427 Numbering: – Type: volume Value: 210 – Type: issue Value: 1/2 Titles: – TitleFull: Journal of Computational & Applied Mathematics Type: main |
| ResultId | 1 |
