Electrical Tree Image De-Noising using Threshold Wavelet Transform and Wiener Filter Article Swipe
Mohamad Nur Khairul Hafizi Rohani
,
Cik Siti Khadijah Abdulah
,
Nur Dini Athirah Gazata
,
Baharuddin Ismail
,
Mohd Anuar Mohd Isa
,
Afifah Shuhada Rosmi
,
Mohamad Kamarol Mohd Jamil
,
Firdaus Muhammad‐Sukki
,
Abdullahi Abubakar Mas’ud
,
Noor Syazwani Mansor
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37934/araset.53.1.7385
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37934/araset.53.1.7385
Electrical treeing occurred in solid dielectric materials, especially in electrical application with high voltage. The occurrence of electrical tree happens when high electric fields applied, causing tiny channels or paths to form. The main issue during the data collection process is the changes of lighting, making it difficult to study the tree's propagation length, fractal dimension, and growth rate due to corrupted images. This research aims to analyse electrical tree structure images in XLPE material using a CCD camera and develop image de-noising techniques to suppress noise on the electrical tree image. The performance was then analysed using the Otsu thresholding algorithm for accurate segmentation. The methodology was divided into four phases: sample preparation, experimental setup, image pre-processing in MATLAB, and testing four de-noising filters: Wiener, median, NLM, and Gaussian. The Wiener filter with higher PSNR, SNR, and RMSE was selected and using superimposed method, both threshold wavelet transforms and wiener was combined to eliminate the noise. Finally, the proposed method of superimposed was tested with the Otsu thresholding method to evaluate accuracy, sensitivity, and specificity of the combination filter. Based on the analysis of PSNR, SNR, and RMSE, the performance of the threshold wavelet and Wiener filter (TWWF) de-noising technique improves the image quality of the electrical tree structure. Thus, for the Otsu thresholding segmentation algorithm analysis, it also had the highest values in terms of accuracy, sensitivity, and specificity.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.37934/araset.53.1.7385
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403084281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403084281Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.37934/araset.53.1.7385Digital Object Identifier
- Title
-
Electrical Tree Image De-Noising using Threshold Wavelet Transform and Wiener FilterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-03Full publication date if available
- Authors
-
Mohamad Nur Khairul Hafizi Rohani, Cik Siti Khadijah Abdulah, Nur Dini Athirah Gazata, Baharuddin Ismail, Mohd Anuar Mohd Isa, Afifah Shuhada Rosmi, Mohamad Kamarol Mohd Jamil, Firdaus Muhammad‐Sukki, Abdullahi Abubakar Mas’ud, Noor Syazwani MansorList of authors in order
- Landing page
-
https://doi.org/10.37934/araset.53.1.7385Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://napier-repository.worktribe.com/file/3884100/1/Electrical%20Tree%20Image%20De-Noising%20using%20Threshold%20Wavelet%20Transform%20and%20Wiener%20FilterDirect OA link when available
- Concepts
-
Wiener filter, Wavelet, Wavelet transform, Artificial intelligence, Wiener deconvolution, Computer science, Computer vision, Mathematics, Pattern recognition (psychology), Algorithm, Deconvolution, Blind deconvolutionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.suppress | 85 |
| abstract_inverted_index.voltage. | 13 |
| abstract_inverted_index.Gaussian. | 129 |
| abstract_inverted_index.accuracy, | 172, 227 |
| abstract_inverted_index.algorithm | 101, 216 |
| abstract_inverted_index.analysis, | 217 |
| abstract_inverted_index.corrupted | 61 |
| abstract_inverted_index.difficult | 47 |
| abstract_inverted_index.eliminate | 154 |
| abstract_inverted_index.lighting, | 44 |
| abstract_inverted_index.structure | 70 |
| abstract_inverted_index.technique | 200 |
| abstract_inverted_index.threshold | 146, 193 |
| abstract_inverted_index.Electrical | 0 |
| abstract_inverted_index.collection | 38 |
| abstract_inverted_index.de-noising | 82, 123, 199 |
| abstract_inverted_index.dielectric | 5 |
| abstract_inverted_index.dimension, | 55 |
| abstract_inverted_index.electrical | 9, 17, 68, 89, 207 |
| abstract_inverted_index.especially | 7 |
| abstract_inverted_index.materials, | 6 |
| abstract_inverted_index.occurrence | 15 |
| abstract_inverted_index.structure. | 209 |
| abstract_inverted_index.techniques | 83 |
| abstract_inverted_index.transforms | 148 |
| abstract_inverted_index.application | 10 |
| abstract_inverted_index.combination | 178 |
| abstract_inverted_index.methodology | 106 |
| abstract_inverted_index.performance | 93, 190 |
| abstract_inverted_index.propagation | 52 |
| abstract_inverted_index.specificity | 175 |
| abstract_inverted_index.experimental | 114 |
| abstract_inverted_index.preparation, | 113 |
| abstract_inverted_index.segmentation | 215 |
| abstract_inverted_index.sensitivity, | 173, 228 |
| abstract_inverted_index.specificity. | 230 |
| abstract_inverted_index.superimposed | 143, 162 |
| abstract_inverted_index.thresholding | 100, 168, 214 |
| abstract_inverted_index.segmentation. | 104 |
| abstract_inverted_index.pre-processing | 117 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.21209082 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |