Wavelet based de-noising using logarithmic shrinkage function
Noise in signals and images can be removed through different de-noising techniques such as mean filtering, median filtering, total variation and filtered variation techniques etc. Wavelet based de-noising is one of the major techniques used for noise removal. In the first part of our work, wavelet t...
| Main Authors: | , , , , , |
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| Format: | Article |
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Springer New York LLC
2018
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| _version_ | 1796864295384907776 |
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| author | Ullah, Hayat Amir, Muhammad Ul Haq, Ihsan Khan, Shafqat Ullah Rahim, M. K. A. Khan, Khan Bahadar |
| author_facet | Ullah, Hayat Amir, Muhammad Ul Haq, Ihsan Khan, Shafqat Ullah Rahim, M. K. A. Khan, Khan Bahadar |
| author_sort | Ullah, Hayat |
| collection | ePrints |
| description | Noise in signals and images can be removed through different de-noising techniques such as mean filtering, median filtering, total variation and filtered variation techniques etc. Wavelet based de-noising is one of the major techniques used for noise removal. In the first part of our work, wavelet transform based logarithmic shrinkage technique is used for de-noising of images, corrupted by noise (during under-sampling in the frequency domain). The logarithmic shrinkage technique is applied to under-sampled Shepp–Logan Phantom image. Experimental results show that the logarithmic shrinkage technique is 7–10% better in PSNR values than the existing classical techniques. In the second part of our work we de-noise the noisy, under-sampled phantom image, having salt and pepper, Gaussian, speckle and Poisson noises through the four thresholding techniques and compute their correlations with the original image. They give the correlation values close to the noisy image. By applying median or wiener filter in parallel with the thresholding techniques, we get 30–35% better results than only applying the thresholding techniques individually. So, in the second part we recover and de-noise the sparse under-sampled images by the combination of shrinkage functions and median filtering or wiener filtering. |
| first_indexed | 2024-03-05T20:39:43Z |
| format | Article |
| id | utm.eprints-86700 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:39:43Z |
| publishDate | 2018 |
| publisher | Springer New York LLC |
| record_format | dspace |
| spelling | utm.eprints-867002020-09-30T09:04:47Z http://eprints.utm.my/86700/ Wavelet based de-noising using logarithmic shrinkage function Ullah, Hayat Amir, Muhammad Ul Haq, Ihsan Khan, Shafqat Ullah Rahim, M. K. A. Khan, Khan Bahadar TK Electrical engineering. Electronics Nuclear engineering Noise in signals and images can be removed through different de-noising techniques such as mean filtering, median filtering, total variation and filtered variation techniques etc. Wavelet based de-noising is one of the major techniques used for noise removal. In the first part of our work, wavelet transform based logarithmic shrinkage technique is used for de-noising of images, corrupted by noise (during under-sampling in the frequency domain). The logarithmic shrinkage technique is applied to under-sampled Shepp–Logan Phantom image. Experimental results show that the logarithmic shrinkage technique is 7–10% better in PSNR values than the existing classical techniques. In the second part of our work we de-noise the noisy, under-sampled phantom image, having salt and pepper, Gaussian, speckle and Poisson noises through the four thresholding techniques and compute their correlations with the original image. They give the correlation values close to the noisy image. By applying median or wiener filter in parallel with the thresholding techniques, we get 30–35% better results than only applying the thresholding techniques individually. So, in the second part we recover and de-noise the sparse under-sampled images by the combination of shrinkage functions and median filtering or wiener filtering. Springer New York LLC 2018 Article PeerReviewed Ullah, Hayat and Amir, Muhammad and Ul Haq, Ihsan and Khan, Shafqat Ullah and Rahim, M. K. A. and Khan, Khan Bahadar (2018) Wavelet based de-noising using logarithmic shrinkage function. Wireless Personal Communications, 98 (1). pp. 1473-1488. ISSN 0929-6212 http://dx.doi.org/10.1007/s11277-017-4927-3 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ullah, Hayat Amir, Muhammad Ul Haq, Ihsan Khan, Shafqat Ullah Rahim, M. K. A. Khan, Khan Bahadar Wavelet based de-noising using logarithmic shrinkage function |
| title | Wavelet based de-noising using logarithmic shrinkage function |
| title_full | Wavelet based de-noising using logarithmic shrinkage function |
| title_fullStr | Wavelet based de-noising using logarithmic shrinkage function |
| title_full_unstemmed | Wavelet based de-noising using logarithmic shrinkage function |
| title_short | Wavelet based de-noising using logarithmic shrinkage function |
| title_sort | wavelet based de noising using logarithmic shrinkage function |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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