Detection of Gaussian noise and its level using deep convolutional neural network

Chuah, J.H. and Khaw, H.Y. and Soon, F.C. and Chow, C.O. (2017) Detection of Gaussian noise and its level using deep convolutional neural network. In: 2017 IEEE Region 10 Conference (TENCON), 5-8 November 2017, Penang, Malaysia.

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Abstract

This study presents a Convolutional Neural Network (CNN) model to effectively recognize the presence of Gaussian noise and its level in images. The existing denoising approaches are mostly based on an assumption that the images to be processed are corrupted with noises. This work, on the other hand, aims to intelligently evaluate if an image is corrupted, and to which level it is degraded, before applying denoising algorithms. We used 12000 and 3000 standard test images for training and testing purposes, respectively. Different noise levels are introduced to these images. The overall accuracy of 74.7% in classifying 10 classes of noise levels are obtained. Our experiments and results have proven that this model is capable of performing Gaussian noise detection and its noise level classification.

Item Type:	Conference or Workshop Item (Paper)
Funders:	Fundamental Research Grant Scheme (FRGS) grant from Malaysian Ministry of Higher Education [FRGS/1/2016/ICT03/UM/02/1]
Additional Information:	Conference paper
Uncontrolled Keywords:	Image noise; Noise detection; Convolutional neural networks; Training Gaussian noise
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering
Depositing User:	Mr. Mohd Safri
Date Deposited:	17 Aug 2018 04:02
Last Modified:	17 Aug 2018 04:04
URI:	http://eprints.um.edu.my/id/eprint/18965

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