Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown

Journal Article

Recent work on Approximate Message Passing algorithms in compressed sensing focuses on 'ideal' algorithms which at each iteration face a subproblem of recovering an unknown sparse signal in Gaussian white noise. The noise level in each subproblem changes from iteration to iteration in a way that depends on the underlying signal (which we don't know!). For such algorithms to be used in practice, it seems we need an estimator that achieves the MMSE when the noise level is unknown. In this paper we solve this problem using convex optimization, Stein Unbiased Risk Estimates and Huber Splines. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Donoho, D; Reeves, G

Published Date

  • January 1, 2013

Published In

Start / End Page

  • 101 - 105

International Standard Serial Number (ISSN)

  • 2157-8095

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2013.6620196

Citation Source

  • Scopus