Compressed sensing in noisy imaging environments

Jarvis Haupt, Rui Castro, Robert Nowak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Compressive Sampling, or Compressed Sensing, has recently generated a tremendous amount of excitement in the image processing community. Compressive Sampling involves taking a relatively small number of non-traditional samples in the form of projections of the signal onto random basis elements or random vectors (random projections). Recent results show that such observations can contain most of the salient information in the signal. It follows that if a signal is compressible in some basis, then a very accurate reconstruction can be obtained from these observations. In many cases this reconstruction is much more accurate than is possible using an equivalent number of conventional point samples. This paper motivates the use of Compressive Sampling for imaging, presents theory predicting reconstruction error rates, and demonstrates its performance in electronic imaging with an example.

Original languageEnglish (US)
Title of host publicationComputational Imaging IV - Proceedings of SPIE-IS and T Electronic Imaging
DOIs
StatePublished - Apr 17 2006
Externally publishedYes
EventComputational Imaging IV - San Jose, CA, United States
Duration: Jan 16 2006Jan 18 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6065
ISSN (Print)0277-786X

Conference

ConferenceComputational Imaging IV
CountryUnited States
CitySan Jose, CA
Period1/16/061/18/06

Keywords

  • Noisy Compressed Sensing
  • Random Projections

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