Images are often corrupted by impulse noise due to noisy sensors or channel transmission errors. In removing impulse noise from the acquired images, various linear and nonlinear filtering methods have been employed by various researchers. These have the drawback of blurring fine details and destroying image edges during noise filtering. In order to overcome this limitation without compromising the useful information content of the digital image, an enhanced AMF and ACWMF impulse noise removal technique by the combination of Artificial Neural Network (ANN) and nonlinear filters. ANN with a back propagation training algorithm was employed at the first stage to detect the impulse noise from the acquired digital images. The detected impulse in the digital image was removed at the second stage of the filtering using Adaptive Centre Weight Median Filter (ACWMF) and Adaptive Median Filter (AMF). mean-square error (MSE), root mean-square error (RMSE) and the peak signal to noise ratio (PSNR) were used for performance evaluation with respect to the percentage of the noise in the corrupted image, and the result showed improvement both in quantitative measures of signal restoration and judgment of image quality.
| Published in | Engineering Science (Volume 1, Issue 1) |
| DOI | 10.11648/j.es.20160101.12 |
| Page(s) | 6-14 |
| Creative Commons |
This is an Open Access article, 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 or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Digital Image, Adaptive Centre Weight Median Filter (ACWMF), Adaptive Median Filter (AMF), Mean-Square Error (MSE), Root Mean-Square Error (RMSE), The Peak Signal to Noise Ratio (PSNR), Artificial Neural Network (ANN)
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APA Style
Olanrewaju Ajanaku, D. O. Aborisade, G. A. Ibitola. (2016). Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality. Engineering Science, 1(1), 6-14. https://doi.org/10.11648/j.es.20160101.12
ACS Style
Olanrewaju Ajanaku; D. O. Aborisade; G. A. Ibitola. Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality. Eng. Sci. 2016, 1(1), 6-14. doi: 10.11648/j.es.20160101.12
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Download@article{10.11648/j.es.20160101.12,
author = {Olanrewaju Ajanaku and D. O. Aborisade and G. A. Ibitola},
title = {Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality},
journal = {Engineering Science},
volume = {1},
number = {1},
pages = {6-14},
doi = {10.11648/j.es.20160101.12},
url = {https://doi.org/10.11648/j.es.20160101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.es.20160101.12},
abstract = {Images are often corrupted by impulse noise due to noisy sensors or channel transmission errors. In removing impulse noise from the acquired images, various linear and nonlinear filtering methods have been employed by various researchers. These have the drawback of blurring fine details and destroying image edges during noise filtering. In order to overcome this limitation without compromising the useful information content of the digital image, an enhanced AMF and ACWMF impulse noise removal technique by the combination of Artificial Neural Network (ANN) and nonlinear filters. ANN with a back propagation training algorithm was employed at the first stage to detect the impulse noise from the acquired digital images. The detected impulse in the digital image was removed at the second stage of the filtering using Adaptive Centre Weight Median Filter (ACWMF) and Adaptive Median Filter (AMF). mean-square error (MSE), root mean-square error (RMSE) and the peak signal to noise ratio (PSNR) were used for performance evaluation with respect to the percentage of the noise in the corrupted image, and the result showed improvement both in quantitative measures of signal restoration and judgment of image quality.},
year = {2016}
}
TY - JOUR T1 - Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality AU - Olanrewaju Ajanaku AU - D. O. Aborisade AU - G. A. Ibitola Y1 - 2016/12/21 PY - 2016 N1 - https://doi.org/10.11648/j.es.20160101.12 DO - 10.11648/j.es.20160101.12 T2 - Engineering Science JF - Engineering Science JO - Engineering Science SP - 6 EP - 14 PB - Science Publishing Group SN - 2578-9279 UR - https://doi.org/10.11648/j.es.20160101.12 AB - Images are often corrupted by impulse noise due to noisy sensors or channel transmission errors. In removing impulse noise from the acquired images, various linear and nonlinear filtering methods have been employed by various researchers. These have the drawback of blurring fine details and destroying image edges during noise filtering. In order to overcome this limitation without compromising the useful information content of the digital image, an enhanced AMF and ACWMF impulse noise removal technique by the combination of Artificial Neural Network (ANN) and nonlinear filters. ANN with a back propagation training algorithm was employed at the first stage to detect the impulse noise from the acquired digital images. The detected impulse in the digital image was removed at the second stage of the filtering using Adaptive Centre Weight Median Filter (ACWMF) and Adaptive Median Filter (AMF). mean-square error (MSE), root mean-square error (RMSE) and the peak signal to noise ratio (PSNR) were used for performance evaluation with respect to the percentage of the noise in the corrupted image, and the result showed improvement both in quantitative measures of signal restoration and judgment of image quality. VL - 1 IS - 1 ER -
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