Performance bounds for expander-based compressed sensing in the presence of Poisson noise

Published

Journal Article

This paper provides performance bounds for compressed sensing in the presence of Poisson noise using expander graphs. The Poisson noise model is appropriate for a variety of applications, including low-light imaging and digital streaming, where the signal-independent and/or bounded noise models used in the compressed sensing literature are no longer applicable. In this paper, we develop a novel sensing paradigm based on expander graphs and propose a MAP algorithm for recovering sparse or compressible signals from Poisson observations. The geometry of the expander graphs and the positivity of the corresponding sensing matrices play a crucial role in establishing the bounds on the signal reconstruction error of the proposed algorithm. The geometry of the expander graphs makes them provably superior to random dense sensing matrices, such as Gaussian or partial Fourier ensembles, for the Poisson noise model.We support our results with experimental demonstrations. © 2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Jafarpour, S; Willett, R; Raginsky, M; Calderbank, R

Published Date

  • December 1, 2009

Published In

Start / End Page

  • 513 - 517

International Standard Serial Number (ISSN)

  • 1058-6393

Digital Object Identifier (DOI)

  • 10.1109/ACSSC.2009.5469879

Citation Source

  • Scopus