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    Random Filters for Compressive Sampling and Reconstruction

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    Author
    Baraniuk, Richard G.; Wakin, Michael; Duarte, Marco F.; Tropp, Joel A.; Baron, Dror
    Date
    2006-07-24
    Abstract
    We propose and study a new technique for efficiently acquiring and reconstructing signals based on convolution with a fixed FIR filter having random taps. The method is designed for sparse and compressible signals, i.e., ones that are well approximated by a short linear combination of vectors from an orthonormal basis. Signal reconstruction involves a non-linear Orthogonal Matching Pursuit algorithm that we implement efficiently by exploiting the nonadaptive, time-invariant structure of the measurement process. While simpler and more efficient than other random acquisition techniques like Compressed Sensing, random filtering is sufficiently generic to summarize many types of compressible signals and generalizes to streaming and continuous-time signals. Extensive numerical experiments demonstrate its efficacy for acquiring and reconstructing signals sparse in the time, frequency, and wavelet domains, as well as piecewise smooth signals and Poisson processes.
    Description
    Conference Paper
    Citation
    R. G. Baraniuk, M. Wakin, M. F. Duarte, J. A. Tropp and D. Baron, "Random Filters for Compressive Sampling and Reconstruction," vol. 3, 2006.
    Published Version
    http://dx.doi.org/10.1109/ICASSP.2006.1660793
    Keyword
    Orthogonal Matching Pursuit algorithm; DSP for Communications; Orthogonal Matching Pursuit algorithm
    Type
    Conference paper
    Citable link to this page
    https://hdl.handle.net/1911/20399
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    • DSP Publications [508]
    • ECE Publications [1494]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map