Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

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dc.contributor.authorErkan, Uğur
dc.contributor.authorGökrem, Levent
dc.contributor.authorEnginoğlu, Serdar
dc.date.accessioned2025-01-21T14:20:42Z
dc.date.available2025-01-21T14:20:42Z
dc.date.issued2019
dc.description.abstractIn image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.
dc.description.abstractIn image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.
dc.identifier.dergipark495904
dc.identifier.doi10.29137/umagd.495904
dc.identifier.issn1308-5514
dc.identifier.issue2-542
dc.identifier.startpage550
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/766584
dc.identifier.urihttps://dergipark.org.tr/tr/pub/umagd/issue/43865/495904
dc.identifier.urihttps://doi.org/10.29137/umagd.495904
dc.identifier.urihttps://hdl.handle.net/20.500.12587/19259
dc.identifier.volume1
dc.language.isoen
dc.publisherKırıkkale Üniversitesi
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectImage denoising
dc.subjectNoise removal
dc.subjectNonlinear filters
dc.subjectImage denoising
dc.subjectNoise removal
dc.subjectNonlinear filters
dc.subjectNonlinear functions
dc.subjectMatrix algebra
dc.subjectEngineering
dc.titleAdaptive Right Median Filter for Salt-and-Pepper Noise Removal
dc.title.alternativeAdaptive Right Median Filter for Salt-and-Pepper Noise Removal
dc.typeArticle

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