Image denoising using 2-D wavelet algorithm for Gaussian-corrupted confocal microscopy images

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dc.authoridGökdel, Yiğit Dağhan/0000-0003-4634-4733
dc.authorwosidGökdel, Yiğit Dağhan/AAO-4840-2020
dc.contributor.authorGokdag, Yunus Engin
dc.contributor.authorSansal, Firat
dc.contributor.authorGokdel, Y. Daghan
dc.date.accessioned2024-07-18T20:42:30Z
dc.date.available2024-07-18T20:42:30Z
dc.date.issued2019
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractConfocal laser scanning microscopy (CLSM) imaging is a non-invasive optical imaging technique for the examination of the living tissues. CLSM inherently enables in-depth sectioning (z-slices) of the focused specimen. Z-slices of the targeted tissue are gathered by adjusting the focal point on the z-axis into the tissue. Unfortunately, these images can get corrupted with noise of different levels caused by out-of focus light originating from above and below the focal plane. This study proposes a reliable method to indicate and eliminate the additive white Gaussian noise (AWGN) present in real CLSM images of skin tissue. In this work, a denoising algorithm using discrete wavelet transform (DWT) is developed in order to remove the noise by preserving the energy conservation. The effect and performance of different wavelet thresholding algorithms are compared and studied along with different tuning parameters. The selection of components employed in the algorithm affects the noise reduction performance therefore, a systematic approach is presented to obtain and utilize the best combination of these parameter values. Analysis of variance (ANOVA) is exploited to inspect the main and the interaction effects of treated parameters. Computational results show the effectiveness of the methodical tuning approach to CLSM image denoising. (C) 2019 Published by Elsevier Ltd.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [SBAG 1135114]en_US
dc.description.sponsorshipThis research is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under the grant number of SBAG 1135114. We specially thank and acknowledge the staff of Department of Dermatology of Istanbul Education and Research Hospital, Turkey.en_US
dc.identifier.doi10.1016/j.bspc.2019.101594
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85068971125en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2019.101594
dc.identifier.urihttps://hdl.handle.net/11411/7298
dc.identifier.volume54en_US
dc.identifier.wosWOS:000488140200005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWavelet Denoisingen_US
dc.subjectThresholdingen_US
dc.subjectNoise Estimationen_US
dc.subjectSkin İmagingen_US
dc.subjectConfocal Microscopyen_US
dc.subjectGeneralized Cross-Validationen_US
dc.subjectNoise-Level Estimationen_US
dc.subjectSkinen_US
dc.titleImage denoising using 2-D wavelet algorithm for Gaussian-corrupted confocal microscopy imagesen_US
dc.typeArticleen_US

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