Deblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask

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dc.authorscopusidTofigh Allahviranloo / 8834494700
dc.authorwosidTofigh Allahviranloo / V-4843-2019
dc.contributor.authorSatvati, Mohammad Amin
dc.contributor.authorLakestani, Mehrdad
dc.contributor.authorKhamnei, Hossein Jabbari
dc.contributor.authorAllahviranloo, Tofigh
dc.date.accessioned2025-05-09T10:05:40Z
dc.date.available2025-05-09T10:05:40Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractIn this paper, we propose a novel image deblurring approach that utilizes a new mask based on the Grünwald-Letnikov fractional derivative. We employ the first five terms of the Grünwald-Letnikov fractional derivative to construct three masks corresponding to the horizontal, vertical, and diagonal directions. Using these matrices, we generate eight additional matrices of size 5 × 5 for eight different orientations: kπ4 , where k = 0, 1, 2, . . ., 7. By combining these eight matrices, we construct a 9 × 9 mask for image deblurring that relates to the order of the fractional derivative. We then categorize images into three distinct regions: smooth areas, textured regions, and edges, utilizing the Wakeby distribution for segmentation. Next, we determine an optimal fractional derivative value tailored to each image category to effectively construct masks for image deblurring. We applied the constructed mask to deblur eight brain images affected by blur. The effectiveness of our approach is demonstrated through evaluations using several metrics, including PSNR, AMBE, and Entropy. By comparing our results to those of other methods, we highlight the efficiency of our technique in image restoration. © 2024 Vilnius University.
dc.identifier.citationSatvati, M. A., Lakestani, M., Khamnei, H. J., & Allahviranloo, T. (2024). Deblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask. Informatica, 35(4), 817-836.
dc.identifier.doi10.15388/24-INFOR573
dc.identifier.endpage836
dc.identifier.issn08684952
dc.identifier.issue4
dc.identifier.scopusqualityQ1
dc.identifier.startpage817
dc.identifier.urihttp://dx.doi.org/10.15388/24-INFOR573
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7247
dc.identifier.volume35
dc.identifier.wosWOS:001380518600006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAllahviranloo, Tofigh
dc.institutionauthoridTofigh Allahviranloo / 0000-0002-6673-3560
dc.language.isoen
dc.publisherIOS Press BV
dc.relation.ispartofInformatica (Netherlands)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGradient Matrix
dc.subjectGrünwald-Letnikov Fractional Derivatives
dc.subjectWakeby Distribution
dc.titleDeblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask
dc.typeArticle

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