A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

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.

Keywords:

Denoising, Autoencoder Salt&pepper noise, Gaussian noise,

Tam Metin

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Bibtex	@araştırma makalesi { gujs1051655, journal = {Gazi University Journal of Science}, eissn = {2147-1762}, address = {}, publisher = {Gazi Üniversitesi}, year = {2023}, volume = {36}, number = {3}, pages = {1122 - 1138}, doi = {10.35378/gujs.1051655}, title = {A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder}, key = {cite}, author = {Darıcı, Muazzez Buket and Erdem, Zeki} }
APA	Darıcı, M. B. & Erdem, Z. (2023). A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder . Gazi University Journal of Science , 36 (3) , 1122-1138 . DOI: 10.35378/gujs.1051655
MLA	Darıcı, M. B. , Erdem, Z. "A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder" . Gazi University Journal of Science 36 (2023 ): 1122-1138 <
Chicago	Darıcı, M. B. , Erdem, Z. "A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder". Gazi University Journal of Science 36 (2023 ): 1122-1138
RIS	TY - JOUR T1 - A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder AU - Muazzez BuketDarıcı, ZekiErdem Y1 - 2023 PY - 2023 N1 - doi: 10.35378/gujs.1051655 DO - 10.35378/gujs.1051655 T2 - Gazi University Journal of Science JF - Journal JO - JOR SP - 1122 EP - 1138 VL - 36 IS - 3 SN - -2147-1762 M3 - doi: 10.35378/gujs.1051655 UR - Y2 - 2022 ER -
EndNote	%0 Gazi University Journal of Science A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder %A Muazzez Buket Darıcı , Zeki Erdem %T A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder %D 2023 %J Gazi University Journal of Science %P -2147-1762 %V 36 %N 3 %R doi: 10.35378/gujs.1051655 %U 10.35378/gujs.1051655
ISNAD	Darıcı, Muazzez Buket , Erdem, Zeki . "A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder". Gazi University Journal of Science 36 / 3 (Eylül 2023): 1122-1138 .
AMA	Darıcı M. B. , Erdem Z. A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder. Gazi University Journal of Science. 2023; 36(3): 1122-1138.
Vancouver	Darıcı M. B. , Erdem Z. A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder. Gazi University Journal of Science. 2023; 36(3): 1122-1138.
IEEE	M. B. Darıcı ve Z. Erdem , "A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder", , c. 36, sayı. 3, ss. 1122-1138, Eyl. 2023, doi:10.35378/gujs.1051655