| Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning |
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| 作者姓名： | 张德祥 高清维 陈军宁 |
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| 作者单位： | [1]Key Lab. of Intelligent Computing and Signal Processing, Anhui University Hefei 230039 China [2]School of Electronic Science and Technology, Anhui University Hefei 230039 China |
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| 基金项目： | Supported by the Education Foundation of Anhui Province (No.2005kj058) |
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| 摘 要： | A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed （MGD） modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeclding algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena.
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| 关 键 词： | 离散微波传输 人造孔径雷达 循环旋转 收缩率 |
| 收稿时间： | 2006-01-10 |
Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning |
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| ZHANG De-xiang,GAO Qing-wei,CHEN Jun-ning.Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning[J].Journal of Electronic Science Technology of China,2006,4(2):127-131. |
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| Authors: | ZHANG De-xiang GAO Qing-wei CHEN Jun-ning |
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| Abstract: | A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed (MGD) modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeckling algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena. |
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| Keywords: | discrete wavelet transform Synthetic Aperture Radar (SAR) despeckling cycle spinning BayesShrink |
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