Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images
Abstract Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholding-based methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-01-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13640-018-0244-3 |
| _version_ | 1811226165898117120 |
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| author | Nikou Farhangi Sedigheh Ghofrani |
| author_facet | Nikou Farhangi Sedigheh Ghofrani |
| author_sort | Nikou Farhangi |
| collection | DOAJ |
| description | Abstract Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholding-based methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It has been shown that the coefficients of nonsubsampled shearlet transform (NSST) are sparser than those of stationary wavelet transform (SWT) for either clean or noisy images. Therefore, it is expected that thresholding-based methods in NSST outperform those in the SWT domain. In this paper, BayesShrink, BiShrink, weighted BayesShrink, and weighted BiShrink in NSST and SWT domains are compared in terms of subjective and objective image assessment. As BayesShrink try to find the optimum threshold for every subband, BiShrink uses coefficients, name “parent,” to clean up coefficients called “child,” and the weighted methods consider the coefficients’ noise efficiency, which implies that subbands in the transform domain may be affected by noise differently. Two models for considering the parent in the NSST domain are proposed. In addition, for both BayesShrink and BiShrink, considering the weighting factor (coefficients noise efficiency) would improve the performance of the corresponding methods as well. Experimental results show that the weighted-BiShrink despeckling approach in the NSST domain gives an outstanding performance when tested with both artificially speckled images and real SAR images. |
| first_indexed | 2024-04-12T09:19:39Z |
| format | Article |
| id | doaj.art-bcad0d6638f549df877e2ad8340d4278 |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-04-12T09:19:39Z |
| publishDate | 2018-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-bcad0d6638f549df877e2ad8340d42782022-12-22T03:38:41ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812018-01-012018111810.1186/s13640-018-0244-3Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR ImagesNikou Farhangi0Sedigheh Ghofrani1Electrical and Electronic Engineering Department, Islamic Azad University, South Tehran BranchElectrical and Electronic Engineering Department, Islamic Azad University, South Tehran BranchAbstract Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholding-based methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It has been shown that the coefficients of nonsubsampled shearlet transform (NSST) are sparser than those of stationary wavelet transform (SWT) for either clean or noisy images. Therefore, it is expected that thresholding-based methods in NSST outperform those in the SWT domain. In this paper, BayesShrink, BiShrink, weighted BayesShrink, and weighted BiShrink in NSST and SWT domains are compared in terms of subjective and objective image assessment. As BayesShrink try to find the optimum threshold for every subband, BiShrink uses coefficients, name “parent,” to clean up coefficients called “child,” and the weighted methods consider the coefficients’ noise efficiency, which implies that subbands in the transform domain may be affected by noise differently. Two models for considering the parent in the NSST domain are proposed. In addition, for both BayesShrink and BiShrink, considering the weighting factor (coefficients noise efficiency) would improve the performance of the corresponding methods as well. Experimental results show that the weighted-BiShrink despeckling approach in the NSST domain gives an outstanding performance when tested with both artificially speckled images and real SAR images.http://link.springer.com/article/10.1186/s13640-018-0244-3BayesShrinkBiShrinkWeighted BayesShrinkWeighted BiShrinkNonsubsampled shearlet transformStationary wavelet transform |
| spellingShingle | Nikou Farhangi Sedigheh Ghofrani Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images EURASIP Journal on Image and Video Processing BayesShrink BiShrink Weighted BayesShrink Weighted BiShrink Nonsubsampled shearlet transform Stationary wavelet transform |
| title | Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images |
| title_full | Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images |
| title_fullStr | Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images |
| title_full_unstemmed | Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images |
| title_short | Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images |
| title_sort | using bayesshrink bishrink weighted bayesshrink and weighted bishrink in nsst and swt for despeckling sar images |
| topic | BayesShrink BiShrink Weighted BayesShrink Weighted BiShrink Nonsubsampled shearlet transform Stationary wavelet transform |
| url | http://link.springer.com/article/10.1186/s13640-018-0244-3 |
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