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https://hdl.handle.net/20.500.14365/5238
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Direk, Tugay | - |
dc.date.accessioned | 2024-03-30T11:21:40Z | - |
dc.date.available | 2024-03-30T11:21:40Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1863-1703 | - |
dc.identifier.uri | https://doi.org/10.1007/s11760-024-03086-4 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14365/5238 | - |
dc.description.abstract | Non-local means (NL-Means) algorithm which removes the noise from the image have been used in the field widely due to its good performance especially for magnetic resonance images which consists of three dimensional data. Its main idea is using all the pixels which are local and non-local in an image and taking weighted averaging of all values. One negative side of this method is that it considers all pixels in the image without looking at their similarity. This paper proposes an NL-Means algorithm with pixel selection by applying linear regression analysis using root mean squared error (RMSE) value. After regression analysis, RMSE of the neighborhoods is used to exclude non-similar pixels during the noise removal. Lastly, obtained results were compared by four different methods which are NL-Means algorithm and, Gaussian, anisotropic diffusion and median filterings. All of the methods were outperformed by our method on structured similarity index and peak signal-to-noise ratio quantitative metrics. Moreover, the level of increase on visual qualities are also represented as a qualitative analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Signal, Image and Video Processing | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Image denoising | en_US |
dc.subject | Image processing | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Noise removal | en_US |
dc.subject | Pixel selection | en_US |
dc.subject | Smoothing filter | en_US |
dc.subject | Image denoising | en_US |
dc.subject | Learning algorithms | en_US |
dc.subject | Magnetic resonance | en_US |
dc.subject | Magnetic resonance imaging | en_US |
dc.subject | Mean square error | en_US |
dc.subject | Median filters | en_US |
dc.subject | Pixels | en_US |
dc.subject | Quality control | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Signal to noise ratio | en_US |
dc.subject | Images processing | en_US |
dc.subject | Local mean | en_US |
dc.subject | Machine-learning | en_US |
dc.subject | Noises removal | en_US |
dc.subject | Nonlocal | en_US |
dc.subject | Performance | en_US |
dc.subject | Pixel selection | en_US |
dc.subject | Root mean squared errors | en_US |
dc.subject | Smoothing filters | en_US |
dc.subject | Three-dimensional data | en_US |
dc.subject | Machine learning | en_US |
dc.title | Image denoising by linear regression on non-local means algorithm | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s11760-024-03086-4 | - |
dc.identifier.scopus | 2-s2.0-85187672572 | en_US |
dc.department | İzmir Ekonomi Üniversitesi | en_US |
dc.authorscopusid | 58880204100 | - |
dc.identifier.wos | WOS:001182210700001 | en_US |
dc.institutionauthor | Direk, T. | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
crisitem.author.dept | 05.04. Software Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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