Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

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dc.authoridMemis, Samet/0000-0002-0958-5872
dc.authoridErkan, Ugur/0000-0002-2481-0230
dc.authoridEnginoglu, Serdar/0000-0002-7188-9893
dc.contributor.authorEnginoglu, Serdar
dc.contributor.authorErkan, Ugur
dc.contributor.authorMemis, Samet
dc.date.accessioned2025-01-27T21:01:33Z
dc.date.available2025-01-27T21:01:33Z
dc.date.issued2019
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.
dc.description.sponsorshipOffice of Scientific Research Projects Coordination at Canakkale Onsekiz Mart University [FHD-2018-1409]
dc.description.sponsorshipThis work was supported by the Office of Scientific Research Projects Coordination at Canakkale Onsekiz Mart University, Grant number: FHD-2018-1409.
dc.identifier.doi10.1007/s11042-019-08110-1
dc.identifier.endpage35418
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85072219193
dc.identifier.scopusqualityQ1
dc.identifier.startpage35401
dc.identifier.urihttps://doi.org/10.1007/s11042-019-08110-1
dc.identifier.urihttps://hdl.handle.net/20.500.12428/27095
dc.identifier.volume78
dc.identifier.wosWOS:000504051800052
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectSalt-and-pepper noise
dc.subjectNon-linear functions
dc.subjectNoise removal
dc.subjectMatrix algebra
dc.subjectImage denoising
dc.subjectRiesz mean
dc.titlePixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal
dc.typeArticle

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