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.
['Zhaoshui He', 'Andrzej Cichocki', 'Rafal Zdunek', 'Jianting Cao']
CG-M-FOCUSS and Its Application to Distributed Compressed Sensing