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Proceedings Paper

Compressed sensing in noisy imaging environments
Author(s): Jarvis Haupt; Rui Castro; Robert Nowak
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Paper 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.

Paper Details

Date Published: 2 February 2006
PDF: 9 pages
Proc. SPIE 6065, Computational Imaging IV, 606507 (2 February 2006); doi: 10.1117/12.659232
Show Author Affiliations
Jarvis Haupt, Univ. of Wisconsin at Madison (United States)
Rui Castro, Rice Univ. (United States)
Robert Nowak, Univ. of Wisconsin at Madison (United States)


Published in SPIE Proceedings Vol. 6065:
Computational Imaging IV
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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