Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Introduction to Digital Image Processing With Matlab
An Introduction to Digital Image Processing With Matlab
Blind source separation using order statistics
Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
Mutual information approach to blind separation of stationary sources
IEEE Transactions on Information Theory
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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This paper focuses on the underdetermined blind signal separation problem with sparse representation. The algorithm is proposed to identify the parameters of mixing model which are unknown. The distribution of mixtures are mapping to a new histogram domain by Hough transform which converts the Cartesian image space to the normal parameterization. And then, fuzzy k-means clustering is employed to seek the cluster centers, i.e. parameters of mixing model, on the histogram. Obtaining accurate estimates, the sources can be recovered clearly. The proposed algorithm and three existing algorithms are tested in the simulations. By the simulation results, our algorithm is able to perform a nice accuracy of estimation through a very low computational consumption.