Blind Source Separation Using Multinode Sparse Representation

The blind source separation problem is concerned with extraction of the underlying source signals from a set of their linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in their representation according to some signal dictionary, dramatically improves the quality of separation. It is especially useful in image processing problems, wherein signals possess strong spatial sparsity In this work we use the multiscale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity We use this intrinsic property for selecting the best (most sparse) subsets of features for further separation. Experiments with ID signals and images demonstrate significant improvement of separation quality.