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An effective exploitation of Volterra Filter for denoising MRI Images [articol]

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dc.contributor.author SugelAnandh, O.
dc.contributor.author Allwin, S.
dc.date.accessioned 2024-10-02T10:38:25Z
dc.date.available 2024-10-02T10:38:25Z
dc.date.issued 2019
dc.identifier.citation SugelAnandh, O.; Allwin, S.: An effective exploitation of Volterra Filter for denoising MRI Images. Timişoara: Editura Politehnica, 2019. en_US
dc.identifier.issn 1582-4594
dc.identifier.uri https://dspace.upt.ro/xmlui/handle/123456789/6700
dc.description.abstract Image 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.iso en en_US
dc.publisher Timișoara : Editura Politehnica en_US
dc.relation.ispartofseries Journal of Electrical Engineering;Vol 19 No 5
dc.subject Magnetic resonance imaging en_US
dc.subject Volterra filter en_US
dc.subject Peak signal to noise ratio en_US
dc.subject Linear minimum mean squared error method en_US
dc.subject Histogram based denoising en_US
dc.subject Wiener filter en_US
dc.subject Maximum likelihood approach en_US
dc.title An effective exploitation of Volterra Filter for denoising MRI Images [articol] en_US
dc.type Article en_US


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