Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain
Wavelet transform has become a very important tool in the field of image denoising. A wavelet transform is a localised analysis of time (space) frequency. It uses a telescopic translation operation to gradually multi-scale refine the signal function and finally adapt to time frequency. One popular a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-07-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0194 |
| _version_ | 1819069264871555072 |
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| author | Wen-quan Fan Wen-shu Xiao Wen-shu Xiao |
| author_facet | Wen-quan Fan Wen-shu Xiao Wen-shu Xiao |
| author_sort | Wen-quan Fan |
| collection | DOAJ |
| description | Wavelet transform has become a very important tool in the field of image denoising. A wavelet transform is a localised analysis of time (space) frequency. It uses a telescopic translation operation to gradually multi-scale refine the signal function and finally adapt to time frequency. One popular approach involves thresholding the wavelet coefficients by using the soft or hard threshold. Another method of image denoising is the Wiener filtering in the wavelet domain. In this study, Gaussian white noise has been added to two grey scale images and the two different denoising methods have been used. By comparing the performance of the two methods, it can be found that the Wiener filtering in the wavelet domain is more prowerful. |
| first_indexed | 2024-12-21T16:47:17Z |
| format | Article |
| id | doaj.art-a82fcc3d486a45acadc5e693bee1ef0a |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-12-21T16:47:17Z |
| publishDate | 2019-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-a82fcc3d486a45acadc5e693bee1ef0a2022-12-21T18:56:58ZengWileyThe Journal of Engineering2051-33052019-07-0110.1049/joe.2019.0194JOE.2019.0194Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domainWen-quan Fan0Wen-shu Xiao1Wen-shu Xiao2First Sector, The Fourteenth Institute CETC (China Electronics Technology Group Corporation)First Sector, The Fourteenth Institute CETC (China Electronics Technology Group Corporation)First Sector, The Fourteenth Institute CETC (China Electronics Technology Group Corporation)Wavelet transform has become a very important tool in the field of image denoising. A wavelet transform is a localised analysis of time (space) frequency. It uses a telescopic translation operation to gradually multi-scale refine the signal function and finally adapt to time frequency. One popular approach involves thresholding the wavelet coefficients by using the soft or hard threshold. Another method of image denoising is the Wiener filtering in the wavelet domain. In this study, Gaussian white noise has been added to two grey scale images and the two different denoising methods have been used. By comparing the performance of the two methods, it can be found that the Wiener filtering in the wavelet domain is more prowerful.https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0194image denoisingwavelet transformsWiener filterswhite noiseimage denoisingwavelet thresholdingWiener filteringwavelet domainwavelet transformtime frequencytelescopic translation operationmultiscale refinewavelet coefficientssoft thresholdhard thresholdgrey scale imagesdifferent denoising methods |
| spellingShingle | Wen-quan Fan Wen-shu Xiao Wen-shu Xiao Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain The Journal of Engineering image denoising wavelet transforms Wiener filters white noise image denoising wavelet thresholding Wiener filtering wavelet domain wavelet transform time frequency telescopic translation operation multiscale refine wavelet coefficients soft threshold hard threshold grey scale images different denoising methods |
| title | Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain |
| title_full | Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain |
| title_fullStr | Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain |
| title_full_unstemmed | Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain |
| title_short | Image denoising based on wavelet thresholding and Wiener filtering in the wavelet domain |
| title_sort | image denoising based on wavelet thresholding and wiener filtering in the wavelet domain |
| topic | image denoising wavelet transforms Wiener filters white noise image denoising wavelet thresholding Wiener filtering wavelet domain wavelet transform time frequency telescopic translation operation multiscale refine wavelet coefficients soft threshold hard threshold grey scale images different denoising methods |
| url | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0194 |
| work_keys_str_mv | AT wenquanfan imagedenoisingbasedonwaveletthresholdingandwienerfilteringinthewaveletdomain AT wenshuxiao imagedenoisingbasedonwaveletthresholdingandwienerfilteringinthewaveletdomain AT wenshuxiao imagedenoisingbasedonwaveletthresholdingandwienerfilteringinthewaveletdomain |