CG-M-FOCUSS and Its Application to Distributed Compressed Sensing

  • Authors:
  • Zhaoshui He;Andrzej Cichocki;Rafal Zdunek;Jianting Cao

  • Affiliations:
  • Lab. for Advanced Brain Signal Processing, Brain Science Institute, Wako-shi, Japan 351-0198 and School of Electronics and Information Engineering, South China University of Technology, Guangzhou, ...;Lab. for Advanced Brain Signal Processing, Brain Science Institute, Wako-shi, Japan 351-0198 and System Research Institute, Polish Academy of Sciences (PAN), Warsaw, Poland 00-901 and Warsaw Unive ...;Institute of Telecommunications, Teleinformatics, and Acoustics, Wroclaw University of Technology, Wroclaw, Poland 50-370;Lab. for Advanced Brain Signal Processing, Brain Science Institute, Wako-shi, Japan 351-0198 and Saitama Institute of Technology, , Saitama, Japan 369-0293

  • Venue:
  • ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
  • Year:
  • 2008

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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.