Open AccessFacial image denoising using AutoEncoder and UNET
Author(s) -
Milan Tripathi
Publication year - 2021
Publication title -
heritage and sustainable development
Language(s) - English
Resource type - Journals
ISSN - 2712-0554
DOI - 10.37868/hsd.v3i2.71
Subject(s) - computer science , autoencoder , artificial intelligence , noise reduction , image (mathematics) , deep learning , image processing , image denoising , pattern recognition (psychology) , image quality , computer vision , artificial neural network , task (project management) , machine learning , engineering , systems engineering
Image denoising is a crucial topic in image processing. Noisy images are generated due to technical and environmental errors. Therefore, it is reasonable to consider image denoising an important topic to study, as it also helps to resolve other image processing issues. However, the challenge is that the classical techniques used are time-consuming and not flexible enough. This article compares the two major neural network architecture which looks promising to resolve this issues. The AutoEncoder and UNET is now the most researched subject in deep learning for image denoising. Multiple model architectures are designed, implement, and evaluated. The dataset is preprocessed and then it is used to train and test the model. It is clearly shown in this paper which model performs the best in this task by comparing both models using the most used parameters to evaluate image quality PSNR and SSIM.