基于复小波域的自适应贝叶斯估计SAR图像相干斑抑制算法  

Adaptive Bayesian-based Speckle Reduction Method in SAR Images using Complex Wavelet Transform

作　　者：刘畅[1] 

机构地区：[1]沈阳职业技术学院,沈阳110045

出　　处：《煤炭技术》2010年第11期203-204,共2页Coal Technology

摘　　要：提出了一种基于复小波域的自适应贝叶斯估计SAR图像相干斑抑制算法。该方法将传统小波的软阈值去加性噪声的特性与双树复小波变换的方向选择性结合起来,可以自适应选择有效的阈值来保留边缘等细节。应用贝叶斯估计来计算复小波系数的最佳值。该估计算子假设信号和噪声的复小波变换系数分别满足阿尔法稳定分布和高斯分布。实验结果表明该算法的去噪性能优于现有的基于离散小波变换的算法。An improved adaptive speckle reduction method is presented based on dual tree complex wavelet transform（CWT）.It combines the characteristics of additive noise reduction of soft thresholding with the CWT＇s directional selectivity,being its main contribution to adapt the effective threshold to preserve the edge detail.A Bayesian estimator is applied to the decomposed data also to estimate the best value for the noise-free complex wavelet coefficients.This estimation is based on alpha-stable and Gaussian distribution hypotheses for complex wavelet coefficients of the signal and noise,respectively.Experimental results show that the denoising performance is among the state-of-the-art techniques based on real discrete wavelet transform（DWT）.

关 键 词：相干斑抑制 双树复小波变换 贝叶斯估计 阿尔法平稳分布 离散小波变换 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]