Signal reconstruction using sparse tree representations

Chinh La, Minh N Do

Research output: Contribution to journalConference article

Abstract

Recent studies in linear inverse problems have recognized the sparse representation of unknown signal in a certain basis as an useful and effective prior information to solve those problems. In many multiscale bases (e.g. wavelets), signals of interest (e.g. piecewise-smooth signals) not only have few significant coefficients, but also those significant coefficients are well-organized in trees. We propose to exploit the tree-structured sparse representation as additional prior information for linear inverse problems with limited numbers of measurements. We present numerical results showing that exploiting the sparse tree representations lead to better reconstruction while requiring less time compared to methods that only assume sparse representations.

Original languageEnglish (US)
Article number59140W
Pages (from-to)1-11
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5914
DOIs
StatePublished - Dec 1 2005
EventWavelets XI - San Diego, CA, United States
Duration: Jul 31 2005Aug 3 2005

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Signal Reconstruction
Signal reconstruction
Sparse Representation
Inverse problems
Linear Inverse Problems
Prior Information
Wavelet Bases
Coefficient
coefficients
Unknown
Numerical Results

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Signal reconstruction using sparse tree representations. / La, Chinh; Do, Minh N.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 5914, 59140W, 01.12.2005, p. 1-11.

Research output: Contribution to journalConference article

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