A novel image denoising technique with Caputo type space-time fractional operators

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dc.authorscopusidMehmet Alper Tunga / 8555922400
dc.authorwosidMehmet Alper Tunga / N-9306-2013
dc.contributor.authorTanrıöver, Evren
dc.contributor.authorKiriş, Ahmet
dc.contributor.authorTunga, Burcu
dc.contributor.authorTunga, Mehmet Alper
dc.date.accessioned2025-04-18T07:22:10Z
dc.date.available2025-04-18T07:22:10Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractA novel image denoising model, namely Full Fractional Total Variation (TVFF), based on the Rudin-Osher-Fatemi (ROF) and the fractional total variation models is presented. The leading advantage of TVFF model is that it uses fractional derivatives with length scale parameters instead of ordinary derivatives with respect to both time and spatial variables in the diffusion equation. The Riesz-Caputo fractional derivative operator is used to disperse nonlocal influence throughout all directions, whereas the Caputo fractional derivative concept is employed for time fractional derivatives. Therefore, the influence of neighboring pixels is given greater weight compared to those situated farther away and this reflects the consideration behind denoising process better. Moreover, the numerical approach is constructed, and its stability and convergence properties are thoroughly examined. To show the superiority of our model, the denoised images are subjected to visual and numerical comparisons using metrics such as the Signal-to-Noise Ratio (SNR), the Structural Similarity Index Measure (SSIM) and the Edge-Retention Ratio (ERR). The performance of the TVFF method is evaluated under various types of noise, including Poisson, Speckle, and Salt & Pepper, and the results are compared with those obtained using Gauss and Median Filters. Furthermore, the proposed method is applied to both blind and synthetic images, thereby showcasing its versatility and applicability across diverse datasets. The outcomes showcase the substantial potential of our enhanced model as a versatile and efficient tool for image denoising.
dc.identifier.citationTanriover, E., Kiris, A., Tunga, B., & Tunga, M. A. (2024). A novel image denoising technique with Caputo type space–time fractional operators. Nonlinear Dynamics, 112(21), 19487-19513.
dc.identifier.doi10.1007/s11071-024-10087-y
dc.identifier.endpage19513
dc.identifier.issn0924-090X
dc.identifier.issn1573-269X
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85200661236
dc.identifier.scopusqualityQ1
dc.identifier.startpage19487
dc.identifier.urihttp://dx.doi.org/10.1007/s11071-024-10087-y
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6445
dc.identifier.volume112
dc.identifier.wosWOS:001286154600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTunga, Mehmet Alper
dc.institutionauthoridMehmet Alper Tunga / 0000-0003-3551-4549
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofNonlinear dynamics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectImage Denoising Model
dc.subjectDiffusion Equation
dc.subjectCaputo Fractional Derivative
dc.subjectRiesz-Caputo Fractional Derivative
dc.subjectFractional Partial Differential Equation
dc.titleA novel image denoising technique with Caputo type space-time fractional operators
dc.typeArticle

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