Abstract
M-FOCUSS is one of the most successful and efficient methods for sparse representation. To reduce the computational cost of M-FOCUSS and to extend its availability for large scale problems, M-FOCUSS is extended to CG-M-FOCUSS by incorporating conjugate gradient (CG) iterations in this paper. Furthermore, the CG-M-FOCUSS is applied to distributed compressed sensing. We illustrate the performance of CG-M-FOCUSS by an MRI image reconstruction example, in which CG-M- FOCUSS can not only reconstruct the MRI image with high precision, but also considerably reduce the computational time. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
He, Z., Cichocki, A., Zdunek, R., & Cao, J. (2008). CG-M-FOCUSS and its application to distributed compressed sensing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 237–245). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_27
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