Denoising of Images Using Autoencoder
DOI:
https://doi.org/10.32628/CSEIT2063122Keywords:
Auto-Encoder, Data Compression, Deep Neural Network.Abstract
Image is the object that stores and reflects visual perception. Images are also important information carriers today. Acquisition channel and artificial editing are the two main ways that corrupt observed images. The goal of image restoration technique is to restore the original image from a noisy observation of it which is aiming to reconstruct a high quality image from its low quality observation has many important applications, like low-level image processing, medical imaging, remote sensing, surveillance, etc. Image denoising is common image restoration problems that are useful by many industrial and scientific applications. The application classifies images based on single image selected from user. The noise from the corrupted image is removed and original clear image is obtained. In our project we are making use of Auto-encoder. Auto-encoder do not need much data pre-processing and it is an end to end training process which helps to remove the noise present in some pictures using some data compression algorithms.
References
- Lovedeep Gondara, 2018, “Medical image denoising using convolutional denoising”, ISSN NO:1608-4667 DOI: 10.1109/ICDMW.2016.0041
- Qian Xiang and Xuliang Pang, 2018“Improved Denoising Auto-encoders for Image Denoising”, ISSN NO:1843-9641DOI:10.1109/CISP-BMEI.2018.8633143
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.