一种剪切波域的稀疏分量分析方法

doi:10.3969/j.issn.1001-2400.2014.01.009

Abstract

Abstract:

In applications of the image blind source separation, the traditional method of Independent Component Analysis(ICA) computes the mixed matrix by using source image directly, without using the prior knowledge that images can be represented sparsely in the transform domain, and it can not lead to a better effect. Based on the capacity of image sparse representation by shearlet, a method of sparse component analysis in the shearlet domain is presented. The image mixed source is first transformed to the shearlet domain and obtains a shearlet coefficient, then the sparsest coefficient is selected by computing kurtosis, and finally the sparse coefficient is used as the input of the ICA method to realize image separation. The complexity of the solving procedure represents a significant decrease since it chooses a less sparse coefficient. Experimental results show that, compared with the traditional ICA method, the method in this paper leads to a better separation effect and shortens the operation time of the algorithm.

Key words: blind source separation, independent component analysis, sparse component analysis, shearlet transform

JI Jian;LI Xiao. Method for sparse component analysis in the shearlet domain[J].J4, 2014, 41(1): 45-52+146.

References

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Method for sparse component analysis in the shearlet domain

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