Image acquisition is an essential step in image processing. When the image acquisition is done the image that is generated is subjected to Impulse noise, Gaussian noise etc. We have performed the image denoising on images inflicted with impulse noise. Image denoising is an essential step in all types of image processing. Traditional techniques reduce the noise in the image but it also reduces the quality of the image. Traditional filters like gaussian filter, median filter is analyzed which work in the spatial domain and filters working in the frequency domain are also considered like Butterworth filters, Weiner filter. A Deep residual Neural Network filter is proposed which is compared with the Fuzzy Neural Network denoiser. Their performance is compared on the metrics PSNR and SSIM. The Fuzzy Neural Network system improves the SSIM significantly compared to a deep residual neural network and a comparison is made with traditional image denoising methods. We also compare the performance of the deep residual neural network, Fuzzy Neural Network system and Median denoising algorithm on impulse noise has been compared. The performance of deep neural networks depends on the total number of examples used and the performance can be improved if we have more image pairs.
| Published in | American Journal of Neural Networks and Applications (Volume 11, Issue 2) |
| DOI | 10.11648/j.ajnna.20251102.13 |
| Page(s) | 58-65 |
| 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), 2025. Published by Science Publishing Group |
Image Denoising, Deep Neural Networks, Fuzzy Logic, SSIM, PSNR
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APA Style
Das, S., Das, D. (2025). A Fuzzy Neural Network System for Denoising Magnetic Resonance Images. American Journal of Neural Networks and Applications, 11(2), 58-65. https://doi.org/10.11648/j.ajnna.20251102.13
ACS Style
Das, S.; Das, D. A Fuzzy Neural Network System for Denoising Magnetic Resonance Images. Am. J. Neural Netw. Appl. 2025, 11(2), 58-65. doi: 10.11648/j.ajnna.20251102.13
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Download@article{10.11648/j.ajnna.20251102.13,
author = {Shubhajoy Das and Debashis Das},
title = {A Fuzzy Neural Network System for Denoising Magnetic Resonance Images
},
journal = {American Journal of Neural Networks and Applications},
volume = {11},
number = {2},
pages = {58-65},
doi = {10.11648/j.ajnna.20251102.13},
url = {https://doi.org/10.11648/j.ajnna.20251102.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20251102.13},
abstract = {Image acquisition is an essential step in image processing. When the image acquisition is done the image that is generated is subjected to Impulse noise, Gaussian noise etc. We have performed the image denoising on images inflicted with impulse noise. Image denoising is an essential step in all types of image processing. Traditional techniques reduce the noise in the image but it also reduces the quality of the image. Traditional filters like gaussian filter, median filter is analyzed which work in the spatial domain and filters working in the frequency domain are also considered like Butterworth filters, Weiner filter. A Deep residual Neural Network filter is proposed which is compared with the Fuzzy Neural Network denoiser. Their performance is compared on the metrics PSNR and SSIM. The Fuzzy Neural Network system improves the SSIM significantly compared to a deep residual neural network and a comparison is made with traditional image denoising methods. We also compare the performance of the deep residual neural network, Fuzzy Neural Network system and Median denoising algorithm on impulse noise has been compared. The performance of deep neural networks depends on the total number of examples used and the performance can be improved if we have more image pairs.
},
year = {2025}
}
TY - JOUR T1 - A Fuzzy Neural Network System for Denoising Magnetic Resonance Images AU - Shubhajoy Das AU - Debashis Das Y1 - 2025/10/28 PY - 2025 N1 - https://doi.org/10.11648/j.ajnna.20251102.13 DO - 10.11648/j.ajnna.20251102.13 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 58 EP - 65 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20251102.13 AB - Image acquisition is an essential step in image processing. When the image acquisition is done the image that is generated is subjected to Impulse noise, Gaussian noise etc. We have performed the image denoising on images inflicted with impulse noise. Image denoising is an essential step in all types of image processing. Traditional techniques reduce the noise in the image but it also reduces the quality of the image. Traditional filters like gaussian filter, median filter is analyzed which work in the spatial domain and filters working in the frequency domain are also considered like Butterworth filters, Weiner filter. A Deep residual Neural Network filter is proposed which is compared with the Fuzzy Neural Network denoiser. Their performance is compared on the metrics PSNR and SSIM. The Fuzzy Neural Network system improves the SSIM significantly compared to a deep residual neural network and a comparison is made with traditional image denoising methods. We also compare the performance of the deep residual neural network, Fuzzy Neural Network system and Median denoising algorithm on impulse noise has been compared. The performance of deep neural networks depends on the total number of examples used and the performance can be improved if we have more image pairs. VL - 11 IS - 2 ER -
Department of Computer Science and Engineering, Techno India University, Kolkata, India
Department of Computer Science and Engineering, Dr Sudhir Chandra Sur Institute of Technology & Sports Complex, Kolkata, India
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