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 |