deposit_your_work

Random Filters for Compressive Sampling and Reconstruction

Files in this item

Files Size Format View
Tro2006May5RandomFilt.PDF 200.7Kb application/pdf Thumbnail

Show full item record

Item Metadata

Title: Random Filters for Compressive Sampling and Reconstruction
Author: Baraniuk, Richard G.; Wakin, Michael; Duarte, Marco F.; Tropp, Joel A.; Baron, Dror
Type: Conference Paper
Keywords: Orthogonal Matching Pursuit algorithm
Citation: R. G. Baraniuk, M. Wakin, M. F. Duarte, J. A. Tropp and D. Baron,"Random Filters for Compressive Sampling and Reconstruction," in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),, pp. III-872 - III-875.
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.
Date Published: 2006-05-01

This item appears in the following Collection(s)

  • ECE Publications [1047 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.