Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown
Publication
, Journal Article
Donoho, D; Reeves, G
Published in: Ieee International Symposium on Information Theory Proceedings
January 1, 2013
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.
Duke Scholars
Published In
Ieee International Symposium on Information Theory Proceedings
DOI
ISSN
2157-8095
Publication Date
January 1, 2013
Start / End Page
101 / 105
Citation
APA
Chicago
ICMJE
MLA
NLM
Donoho, D., & Reeves, G. (2013). Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown. Ieee International Symposium on Information Theory Proceedings, 101–105. https://doi.org/10.1109/ISIT.2013.6620196
Donoho, D., and G. Reeves. “Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown.” Ieee International Symposium on Information Theory Proceedings, January 1, 2013, 101–5. https://doi.org/10.1109/ISIT.2013.6620196.
Donoho D, Reeves G. Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown. Ieee International Symposium on Information Theory Proceedings. 2013 Jan 1;101–5.
Donoho, D., and G. Reeves. “Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown.” Ieee International Symposium on Information Theory Proceedings, Jan. 2013, pp. 101–05. Scopus, doi:10.1109/ISIT.2013.6620196.
Donoho D, Reeves G. Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown. Ieee International Symposium on Information Theory Proceedings. 2013 Jan 1;101–105.
Published In
Ieee International Symposium on Information Theory Proceedings
DOI
ISSN
2157-8095
Publication Date
January 1, 2013
Start / End Page
101 / 105