Research Article | OPEN ACCESS
Noise Detection in Images using Moments
1G. Maragatham, 2S. Md. Mansoor Roomi and 3P. Vasuki
1Department of Electronics and Communication Engineering, Anna University-U.C.E. Dindigul Campus
2Department of Electronics and Communication Engineering, Thiagarajar College of
Engineering Madurai
3Department of Electronics and Communication Engineering, K.L.N. College of Information and Technology, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 3:307-314
Received: December 26, 2014 | Accepted: January 27, 2015 | Published: May 30, 2015
Abstract
Noise is an unwanted signal that disturbs brightness/color information of an image. Image denoising algorithms often are directed by human intervention or assume the type of noise, such approaches are not fully automatic in detecting the presence of noise and also in identifying the type of noise. This study aims to introduce a moment based noise detection and identification technique to detect the presence of noise in an image and if so, whether the noise is impulse. The proposed method uses Discrete Cosine Transform to obtain frequency components over which the Kurtosis is calculated. The perturbation of kurtosis is computed in terms of Sum of Absolute Deviation (SAD). Based on the larger experimentation a threshold value is set to detect the presence of noise and based on the ranges of SAD value, types of noise is identified.
Keywords:
Discrete cosine transforms, impulse noise , kurtosis , sum of absolute deviation,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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