A Comparative Study on Denoising From Facial Images Using Convolutional Autoencoder

dc.authorid Darici, Muazzez Buket/0000-0002-0943-9381
dc.contributor.author Darici, Muazzez Buket
dc.contributor.author Darıcı, Muazzez Buket
dc.contributor.author Erdem, Zeki
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-10-15T19:38:54Z
dc.date.available 2024-10-15T19:38:54Z
dc.date.issued 2023
dc.department Kadir Has University en_US
dc.department-temp [Darici, Muazzez Buket] Kadir Has Univ, Dept Elect Elect Engn, Istanbul, Turkiye; [Erdem, Zeki] Kadir Has Univ, Dept Management Informat Syst, Istanbul, Turkiye en_US
dc.description Darici, Muazzez Buket/0000-0002-0943-9381 en_US
dc.description.abstract Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.citationcount 1
dc.identifier.doi 10.35378/gujs.1051655
dc.identifier.endpage 1138 en_US
dc.identifier.issn 2147-1762
dc.identifier.issue 3 en_US
dc.identifier.scopusquality Q3
dc.identifier.startpage 1122 en_US
dc.identifier.uri https://doi.org/10.35378/gujs.1051655
dc.identifier.uri https://hdl.handle.net/20.500.12469/6293
dc.identifier.volume 36 en_US
dc.identifier.wos WOS:001108851000024
dc.language.iso en en_US
dc.publisher Gazi Univ en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Denoising en_US
dc.subject Autoencoder en_US
dc.subject Salt&pepper noise en_US
dc.subject Gaussian noise en_US
dc.title A Comparative Study on Denoising From Facial Images Using Convolutional Autoencoder en_US
dc.type Article en_US
dc.wos.citedbyCount 0
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