Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning

作     者：张德祥 高清维 陈军宁 

作者机构：Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Electronic Science and Technology Anhui University Hefei 230039 China 

出 版 物：《Journal of Electronic Science and Technology of China》 (中国电子科技（英文版）)

年 卷 期：2006年第4卷第2期

页      面：127-131页

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

基　　金：Supported by the Education Foundation of Anhui Province (No.2005kj058) 

主　　题：discrete wavelet transform Synthetic Aperture Radar (SAR) despeclding cycle spinning BayesShrink 

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.