Abstract：Morphological Component Analysis(MCA) is a novel decomposition method based on sparse representation of signals and images. In this paper, a nonlinear blind source separation algorithm was proposed by extending the linear blind source separation algorithm based on MCA to the nonlinear domain. The mixing signals were first mapped to high dimensional feature space, the nonlinear mixing problem was converted into linear mixing problem, the MCA algorithm was applied to linear mixing signals in the feature space. The experiment results showed that the efficiency of the proposed algorithm.
2012, Vol. 31 
