Abstract
This chapter presents a sparse representation method for single-channel signal separation with a priori knowledge. In this method, it is assumed that different source signals can be represented with different subsets of a dictionary constructed based on some a priori knowledge about these sources. Then, by estimating the sparse representation of the observed signal over this dictionary, we can finally recover the source signals. The two keys of this method are dictionary constructions and pursuit algorithms for finding sparse representations. An overview of commonly used schemes or algorithms for the two keys is given. In our work, this method is used to separate MRS data in order to achieve accurate MRS quantitation. Simulation results show the good performance of this method in separating the overlapping resonances and baseline. Quantitations of in vivo 1H MRS data of human brain tissues and prostate tissues demonstrate the effectiveness of this method.
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Guo, Y., Ruan, S. (2013). Signal Separation with A Priori Knowledge Using Sparse Representation. In: Chatterjee, A., Nobahari, H., Siarry, P. (eds) Advances in Heuristic Signal Processing and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_14
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DOI: https://doi.org/10.1007/978-3-642-37880-5_14
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