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Interferogram Filtering Using Gaussians Scale Mixtures in Steerable Wavelet Domain

机译:可控小波域中使用高斯尺度混合信号的干涉图滤波

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An interferogram filtering is presented in this paper. The main concern of the proposed scheme is to lower the residues count mean while preserving the location and jump height of the lines of phase discontinuity. The proposed method is based on a statistical model of the coefficients of multi-scale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. The performance of this method substantially has the advantages of reducing number of residuals without affecting line of height discontinuity.
机译:本文介绍了干涉图滤波。所提出的方案的主要关注点是在保持相位不连续线的位置和跳变高度的同时降低残差计数平均值。所提出的方法基于面向多尺度的系数的统计模型。相邻位置和比例的系数邻域被建模为两个独立随机变量的乘积:一个高斯向量和一个隐藏的正标量乘数。在此模型下,每个系数的贝叶斯最小二乘估计值将减小为隐藏乘数变量所有可能值上的局部线性估计值的加权平均值。该方法的性能基本上具有减少残差数量而不影响高度不连续线的优点。

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