Please use this identifier to cite or link to this item: https://dspace.upt.ro/xmlui/handle/123456789/6700
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dc.contributor.authorSugelAnandh, O.-
dc.contributor.authorAllwin, S.-
dc.date.accessioned2024-10-02T10:38:25Z-
dc.date.available2024-10-02T10:38:25Z-
dc.date.issued2019-
dc.identifier.citationSugelAnandh, O.; Allwin, S.: An effective exploitation of Volterra Filter for denoising MRI Images. Timişoara: Editura Politehnica, 2019.en_US
dc.identifier.issn1582-4594-
dc.identifier.urihttps://dspace.upt.ro/xmlui/handle/123456789/6700-
dc.description.abstractImage denoising is most effective for achieving both noise reduction and feature preservation. To recover the original image various noise removal techniques such as, linear minimum mean squared error method (LMMSE), histogram based denoising, wiener filter and maximum likelihood (ML) approach are used. The main problem in these filter is resulting images are often blurred and causes spatial flattering. In this paper, Volterra filter is proposed to eliminate the noise to the maximum extent, without altering the quality of an original MRI image. Among all the denoising filters, Volterra shows its excellence with the highest peak signal to noise ratio (PSNR) value and the lowest mean square error value (MSE). The performance is evaluated to validate and estimate the performance of visual quality of an image.en_US
dc.language.isoenen_US
dc.publisherTimișoara : Editura Politehnicaen_US
dc.relation.ispartofseriesJournal of Electrical Engineering;Vol 19 No 5-
dc.subjectMagnetic resonance imagingen_US
dc.subjectVolterra filteren_US
dc.subjectPeak signal to noise ratioen_US
dc.subjectLinear minimum mean squared error methoden_US
dc.subjectHistogram based denoisingen_US
dc.subjectWiener filteren_US
dc.subjectMaximum likelihood approachen_US
dc.titleAn effective exploitation of Volterra Filter for denoising MRI Images [articol]en_US
dc.typeArticleen_US
Appears in Collections:Articole științifice/Scientific articles

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