Medical Image Denoising by Improved Kuan Filter
<em><span lang="EN-GB">This paper focuses on the issue of speckle noise and its suppression. Firstly, the multiplicative speckle noise model and its mathematical formulation are introduced. Then, certain de-noising methods are described together with possible improvements. On t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
VSB-Technical University of Ostrava
2012-01-01
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| Series: | Advances in Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://advances.utc.sk/index.php/AEEE/article/view/529 |
| _version_ | 1797827119087091712 |
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| author | Radek Benes Kamil Riha |
| author_facet | Radek Benes Kamil Riha |
| author_sort | Radek Benes |
| collection | DOAJ |
| description | <em><span lang="EN-GB">This paper focuses on the issue of speckle noise and its suppression. Firstly, the multiplicative speckle noise model and its mathematical formulation are introduced. Then, certain de-noising methods are described together with possible improvements. On their basis, an improvement of Kuan method (KuanS) is proposed. Performance of proposed KuanS method is tested on real ultrasound images and synthetic images corrupted with speckle noise. PSNR, edge preservation, standard deviation of homogenous regions and SIR are used for the evaluation of quality of noise suppression. Performance of the KuanS is compared with other methods. The KuanS method achieves satisfactory results even in comparison with more complex methods (SRAD, wavelet based noise suppression).</span><br /></em> |
| first_indexed | 2024-04-09T12:43:11Z |
| format | Article |
| id | doaj.art-7a853014619f4a1f958c0b21b1b88eab |
| institution | Directory Open Access Journal |
| issn | 1336-1376 1804-3119 |
| language | English |
| last_indexed | 2024-04-09T12:43:11Z |
| publishDate | 2012-01-01 |
| publisher | VSB-Technical University of Ostrava |
| record_format | Article |
| series | Advances in Electrical and Electronic Engineering |
| spelling | doaj.art-7a853014619f4a1f958c0b21b1b88eab2023-05-14T20:50:07ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192012-01-01101434910.15598/aeee.v10i1.529512Medical Image Denoising by Improved Kuan FilterRadek BenesKamil Riha<em><span lang="EN-GB">This paper focuses on the issue of speckle noise and its suppression. Firstly, the multiplicative speckle noise model and its mathematical formulation are introduced. Then, certain de-noising methods are described together with possible improvements. On their basis, an improvement of Kuan method (KuanS) is proposed. Performance of proposed KuanS method is tested on real ultrasound images and synthetic images corrupted with speckle noise. PSNR, edge preservation, standard deviation of homogenous regions and SIR are used for the evaluation of quality of noise suppression. Performance of the KuanS is compared with other methods. The KuanS method achieves satisfactory results even in comparison with more complex methods (SRAD, wavelet based noise suppression).</span><br /></em>http://advances.utc.sk/index.php/AEEE/article/view/529noisespeckle noiseimage de-noisingkuan filterpsnrstandard deviation |
| spellingShingle | Radek Benes Kamil Riha Medical Image Denoising by Improved Kuan Filter Advances in Electrical and Electronic Engineering noise speckle noise image de-noising kuan filter psnr standard deviation |
| title | Medical Image Denoising by Improved Kuan Filter |
| title_full | Medical Image Denoising by Improved Kuan Filter |
| title_fullStr | Medical Image Denoising by Improved Kuan Filter |
| title_full_unstemmed | Medical Image Denoising by Improved Kuan Filter |
| title_short | Medical Image Denoising by Improved Kuan Filter |
| title_sort | medical image denoising by improved kuan filter |
| topic | noise speckle noise image de-noising kuan filter psnr standard deviation |
| url | http://advances.utc.sk/index.php/AEEE/article/view/529 |
| work_keys_str_mv | AT radekbenes medicalimagedenoisingbyimprovedkuanfilter AT kamilriha medicalimagedenoisingbyimprovedkuanfilter |