Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation
The denoising (noise reduction) of a natural image contaminated with Additive and white noise of Gaussian model is an important preprocessing step for many visualization techniques and still a challenging problem for researchers. This paper treats with threshold estimation technique to reduce the no...
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| Format: | Article |
| Language: | English |
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Praise Worthy Prize
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/36977/ http://psasir.upm.edu.my/id/eprint/36977/1/Denosing%20of%20natural%20image%20based%20on%20non.pdf |
| _version_ | 1848848483824435200 |
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| author | Khmag, Asem Ramli, Abdul Rahman Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari |
| author_facet | Khmag, Asem Ramli, Abdul Rahman Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari |
| author_sort | Khmag, Asem |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The denoising (noise reduction) of a natural image contaminated with Additive and white noise of Gaussian model is an important preprocessing step for many visualization techniques and still a challenging problem for researchers. This paper treats with threshold estimation technique to reduce the noise in natural images by using on discrete wavelet transformation. Calculating the value of thresholding, the way it works in the algorithm (derivation of thresholding function) and the type of wavelet mother functions, are pivotal issues in the field of denoising based wavelet approach. In this study the result shows that the proposed denoising algorithm based on semi-soft threshold algorithm outperforms the traditional wavelet denoising techniques in terms of visual quality and subjective scales, where it preserved the edges, ridges details of the reconstructed image and the quality of visualization shape as well. The execution time was taken into consideration as well; it shows that the new algorithm presents competitive results compared with the standard methods such as Wiener filter, SureShrink, Oracle Shrink, BM3D and BayesShrink. To accomplish the denoising process, our algorithm was compared with the various the standard denoising algorithms that were mentioned earlier. |
| first_indexed | 2025-11-15T09:35:13Z |
| format | Article |
| id | upm-36977 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:35:13Z |
| publishDate | 2014 |
| publisher | Praise Worthy Prize |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-369772015-09-17T23:59:05Z http://psasir.upm.edu.my/id/eprint/36977/ Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation Khmag, Asem Ramli, Abdul Rahman Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari The denoising (noise reduction) of a natural image contaminated with Additive and white noise of Gaussian model is an important preprocessing step for many visualization techniques and still a challenging problem for researchers. This paper treats with threshold estimation technique to reduce the noise in natural images by using on discrete wavelet transformation. Calculating the value of thresholding, the way it works in the algorithm (derivation of thresholding function) and the type of wavelet mother functions, are pivotal issues in the field of denoising based wavelet approach. In this study the result shows that the proposed denoising algorithm based on semi-soft threshold algorithm outperforms the traditional wavelet denoising techniques in terms of visual quality and subjective scales, where it preserved the edges, ridges details of the reconstructed image and the quality of visualization shape as well. The execution time was taken into consideration as well; it shows that the new algorithm presents competitive results compared with the standard methods such as Wiener filter, SureShrink, Oracle Shrink, BM3D and BayesShrink. To accomplish the denoising process, our algorithm was compared with the various the standard denoising algorithms that were mentioned earlier. Praise Worthy Prize 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/36977/1/Denosing%20of%20natural%20image%20based%20on%20non.pdf Khmag, Asem and Ramli, Abdul Rahman and Syed Mohamed, Syed Abdul Rahman Al-Haddad and Hashim, Shaiful Jahari (2014) Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation. International Review on Computers and Software, 9 (8). 1348 - 1357. ISSN 1828-6003 |
| spellingShingle | Khmag, Asem Ramli, Abdul Rahman Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title | Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title_full | Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title_fullStr | Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title_full_unstemmed | Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title_short | Denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| title_sort | denosing of natural image based on non-linear threshold filtering using discrete wavelet transformation |
| url | http://psasir.upm.edu.my/id/eprint/36977/ http://psasir.upm.edu.my/id/eprint/36977/1/Denosing%20of%20natural%20image%20based%20on%20non.pdf |