Impulsive noise suppression methods based on time adaptive self-organizing map
Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, v...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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Multidisciplinary Digital Publishing Institute (MDPI)
2023
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| Online Access: | https://eprints.ums.edu.my/id/eprint/42244/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/42244/2/FULL%20TEXT.pdf |
| _version_ | 1825716004541431808 |
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| author | Seyed Hamidreza Hazaveh Ali Bayandour Azam Khalili Ali Barkhordary Ali Farzamnia Ervin Gubin Moung |
| author_facet | Seyed Hamidreza Hazaveh Ali Bayandour Azam Khalili Ali Barkhordary Ali Farzamnia Ervin Gubin Moung |
| author_sort | Seyed Hamidreza Hazaveh |
| collection | UMS |
| description | Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and maintains color information as well as image details. |
| first_indexed | 2025-03-05T01:34:22Z |
| format | Article |
| id | ums.eprints-42244 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2025-03-05T01:34:22Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | ums.eprints-422442024-12-16T03:24:31Z https://eprints.ums.edu.my/id/eprint/42244/ Impulsive noise suppression methods based on time adaptive self-organizing map Seyed Hamidreza Hazaveh Ali Bayandour Azam Khalili Ali Barkhordary Ali Farzamnia Ervin Gubin Moung QA75.5-76.95 Electronic computers. Computer science TA1-2040 Engineering (General). Civil engineering (General) Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and maintains color information as well as image details. Multidisciplinary Digital Publishing Institute (MDPI) 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42244/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/42244/2/FULL%20TEXT.pdf Seyed Hamidreza Hazaveh and Ali Bayandour and Azam Khalili and Ali Barkhordary and Ali Farzamnia and Ervin Gubin Moung (2023) Impulsive noise suppression methods based on time adaptive self-organizing map. Energies, 16. pp. 1-15. https://doi.org/10.3390/en16042034 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science TA1-2040 Engineering (General). Civil engineering (General) Seyed Hamidreza Hazaveh Ali Bayandour Azam Khalili Ali Barkhordary Ali Farzamnia Ervin Gubin Moung Impulsive noise suppression methods based on time adaptive self-organizing map |
| title | Impulsive noise suppression methods based on time adaptive self-organizing map |
| title_full | Impulsive noise suppression methods based on time adaptive self-organizing map |
| title_fullStr | Impulsive noise suppression methods based on time adaptive self-organizing map |
| title_full_unstemmed | Impulsive noise suppression methods based on time adaptive self-organizing map |
| title_short | Impulsive noise suppression methods based on time adaptive self-organizing map |
| title_sort | impulsive noise suppression methods based on time adaptive self organizing map |
| topic | QA75.5-76.95 Electronic computers. Computer science TA1-2040 Engineering (General). Civil engineering (General) |
| url | https://eprints.ums.edu.my/id/eprint/42244/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/42244/2/FULL%20TEXT.pdf |
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