Image De-noising with a New Threshold Value Using Wavelets Article Swipe
Baharuddin Ismail
,
Anjum Khan
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.2012.10(2).749
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.2012.10(2).749
The image de-noising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among the various methods for recovering infinite dimensional objects like curves, densities, images etc. The wavelet techniques are very effective to remove the noise because of its ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. This paper concentrates on selecting a threshold for wavelet function estimation. A new threshold value is pro-posed to shrink the wavelet coefficients obtained by wavelet decomposition of a noisy image by considering that the sub band coefficients have a generalized Gaussian distribution. The proposed threshold value is based on the power of 2 in the size 2^J x 2^J of the data that can be computed efficiently. The experiment has been conducted on various test images to compare with the established threshold parameters. The result shows that the proposed threshold value removes the noise significantly.
Related Topics
Concepts
Wavelet
Wavelet packet decomposition
Stationary wavelet transform
Mathematics
Second-generation wavelet transform
Wavelet transform
Discrete wavelet transform
Artificial intelligence
Pattern recognition (psychology)
Noise (video)
Lifting scheme
Cascade algorithm
Energy (signal processing)
Gaussian noise
Algorithm
Computer science
Image (mathematics)
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://www.airitilibrary.com/Publication/Index/16838602-201204-201404020019-201404020019-259-270
- OA Status
- green
- Cited By
- 11
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2185697096
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2185697096Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.6339/jds.2012.10(2).749Digital Object Identifier
- Title
-
Image De-noising with a New Threshold Value Using WaveletsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-20Full publication date if available
- Authors
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Baharuddin Ismail, Anjum KhanList of authors in order
- Landing page
-
https://www.airitilibrary.com/Publication/Index/16838602-201204-201404020019-201404020019-259-270Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.airitilibrary.com/Publication/Index/16838602-201204-201404020019-201404020019-259-270Direct OA link when available
- Concepts
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Wavelet, Wavelet packet decomposition, Stationary wavelet transform, Mathematics, Second-generation wavelet transform, Wavelet transform, Discrete wavelet transform, Artificial intelligence, Pattern recognition (psychology), Noise (video), Lifting scheme, Cascade algorithm, Energy (signal processing), Gaussian noise, Algorithm, Computer science, Image (mathematics), StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2018: 3, 2017: 2, 2016: 2, 2015: 1, 2014: 2Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.compare | 154 |
| abstract_inverted_index.curves, | 33 |
| abstract_inverted_index.domain. | 76 |
| abstract_inverted_index.methods | 26, 65 |
| abstract_inverted_index.objects | 31 |
| abstract_inverted_index.process | 5 |
| abstract_inverted_index.removes | 168 |
| abstract_inverted_index.values. | 62 |
| abstract_inverted_index.various | 25, 150 |
| abstract_inverted_index.wavelet | 19, 38, 64, 71, 75, 85, 97, 101 |
| abstract_inverted_index.Gaussian | 117 |
| abstract_inverted_index.computed | 142 |
| abstract_inverted_index.function | 86 |
| abstract_inverted_index.infinite | 29 |
| abstract_inverted_index.obtained | 99 |
| abstract_inverted_index.proposed | 120, 165 |
| abstract_inverted_index.conducted | 148 |
| abstract_inverted_index.corrupted | 14 |
| abstract_inverted_index.effective | 42 |
| abstract_inverted_index.naturally | 13 |
| abstract_inverted_index.pro-posed | 93 |
| abstract_inverted_index.selecting | 81 |
| abstract_inverted_index.shrinking | 69 |
| abstract_inverted_index.threshold | 83, 90, 121, 158, 166 |
| abstract_inverted_index.transform | 61 |
| abstract_inverted_index.de-noising | 2 |
| abstract_inverted_index.densities, | 34 |
| abstract_inverted_index.experiment | 145 |
| abstract_inverted_index.recovering | 28 |
| abstract_inverted_index.techniques | 39 |
| abstract_inverted_index.considering | 108 |
| abstract_inverted_index.dimensional | 30 |
| abstract_inverted_index.established | 157 |
| abstract_inverted_index.estimation. | 87 |
| abstract_inverted_index.generalized | 116 |
| abstract_inverted_index.parameters. | 159 |
| abstract_inverted_index.coefficients | 72, 98, 113 |
| abstract_inverted_index.concentrates | 79 |
| abstract_inverted_index.efficiently. | 143 |
| abstract_inverted_index.decomposition | 102 |
| abstract_inverted_index.distribution. | 118 |
| abstract_inverted_index.significantly. | 171 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.00091846 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |