K-hyperline clustering learning for sparse component analysis
Signal Processing
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
A New Algorithm Estimating the Mixing Matrix for the Sparse Component Analysis
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
A New Clustering Algorithm Based on Normalized Signal for Sparse Component Analysis
CIS '10 Proceedings of the 2010 International Conference on Computational Intelligence and Security
Optimality and stability of the K-hyperline clustering algorithm
Pattern Recognition Letters
Hidden Markov models for wavelet-based blind source separation
IEEE Transactions on Image Processing
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Mixing matrix estimation using discriminative clustering for blind source separation
Digital Signal Processing
An algorithm for underdetermined mixing matrix estimation
Neurocomputing
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In the field of blind image separation (BIS) based on the sparse component analysis, separation efficiency and accuracy are directly affected by the number of clustering samples. To address this problem, a new algorithm for the detection of points in the Haar wavelet domain was proposed in which only single source contributions occur. The algorithm identified the single source points (SSPs) by comparing the absolute direction between the diagonal and horizontal components of the Haar wavelet coefficients of mixed images. After screening the SSPs, the wavelet coefficients of the images are sparser. The experimental results showed that, compared to the conventional method, the proposed algorithm could estimate the mixing matrix faster and more accurately, and it allowed identification of latent variables via statistical histograms.