多尺度几何分析旨在构建最优逼近意义下的高维函数表示方法。
The aim of Multiscale Geometric Analysis is to find a kind of optimal representation of high dimension function in the sense of nonlinear approximation.
着重研究第二代Curvelet多尺度几何分析，对信号进行稀疏表示。
And focus on researching the second generation Curvelet sparse representation.
本文通过对于多尺度几何分析工具的研究，根据其特点将其运用到SAR图像处理中。
In this paper, multiscale geometric analysis tool for the research, applying the latest research tool, according to its characteristics be applied to SAR image processing.
他的研究兴趣包括图像和多维信号处理，计算图像，小波和多尺度几何分析，可视化信息的表述。
His research interests include image and multi-dimensional signal processing, computational imaging, wavelets and multiscale geometric analysis, and visual information representation.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
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