A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Authors : Muazzez Buket DARICI, Zeki ERDEM

Pages : 1122-1138

Doi:10.35378/gujs.1051655

View : 149 | Download : 305

Publication Date : 2023-09-01

Article Type : Research Paper

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 insert ignore into journalissuearticles values(Gaussian and Salt&Pepper);. Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(PSNR); and structural similarity index measure insert ignore into journalissuearticles values(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