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Sparse representations and compressive sensing in multi-dimensional signal processing

  • S.I. : Visvesvaraya
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Abstract

Sparse representation is widely used in signal/image reconstruction, denoising, restoration, feature extraction, etc. During data compression most of the low magnitude transform coefficients are thrown away while keeping only the high magnitude coefficients. In some practical applications, data acquisition itself is a major challenge, like, signal acquisition in magnetic resonance imaging (MRI), body area networks (BAN), remote sensing, etc. According to compressed sensing theory if signal/image is sparse in some transform domain and acquired with respect to another basis incoherent to the sparse representation basis then one can reconstruct the underlying signal/image just from a few random projections. Multi-dimensional signal processing involving MRI, BAN and remote sensing images takes significant amount of computational time because of their raw data size. Therefore, state-of-the-art sparse reconstruction algorithms developed for parallel computing with multi-core CPU and GP-GPU is required for realtime or near-realtime implementations.

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Notes

  1. http://old.mridata.org/undersampled/abdomens/.

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  3. Bhuvan, NRSC Open EO Data Archive , (Last seen on 20 April, 2019). https://bhuvan-app3.nrsc.gov.in/data/download/index.php.

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Acknowledgements

Author would like to thank “Visvesvaraya PhD Scheme for Electronics and IT” (Grant No. MLA/MUM/GA/10(37)B dt. 15/01/2018), Ministry of Electronics and Information Technology, Government of India for providing financial support to setup necessary infrastructure besides contingency funds for carrying out this research.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Tezpur, Assam, 784028, India

    Bhabesh Deka

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Deka, B. Sparse representations and compressive sensing in multi-dimensional signal processing. CSIT 7, 233–242 (2019). https://doi.org/10.1007/s40012-019-00242-x

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