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

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

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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.

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Author information

Authors and Affiliations

  1. Lab. for Advanced Brain Signal Processing, Brain Science Institute, Wako-shi, Saitama, 351-0198, Japan

    Zhaoshui He, Andrzej Cichocki & Jianting Cao

  2. School of Electronics and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Zhaoshui He

  3. System Research Institute, Polish Academy of Sciences (PAN), Warsaw, 00-901, Poland

    Andrzej Cichocki

  4. Warsaw University of Technology, Warsaw, 00-661, Poland

    Andrzej Cichocki

  5. Institute of Telecommunications, Teleinformatics, and Acoustics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Rafal Zdunek

  6. Saitama Institute of Technology,  , Saitama, 369-0293, Japan

    Jianting Cao

Authors
  1. Zhaoshui He
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  2. Andrzej Cichocki
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  3. Rafal Zdunek
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  4. Jianting Cao
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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He, Z., Cichocki, A., Zdunek, R., Cao, J. (2008). CG-M-FOCUSS and Its Application to Distributed Compressed Sensing. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_27

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  • DOI: https://doi.org/10.1007/978-3-540-87732-5_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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