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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 20, Iss. 7 — Jul. 1, 2003
  • pp: 1261–1270

Spectral estimation theory: beyond linear but before Bayesian

Jeffrey M. DiCarlo and Brian A. Wandell  »View Author Affiliations


JOSA A, Vol. 20, Issue 7, pp. 1261-1270 (2003)
http://dx.doi.org/10.1364/JOSAA.20.001261


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Abstract

Most color-acquisition devices capture spectral signals by acquiring only three samples, critically undersampling the spectral information. We analyze the problem of estimating high-dimensional spectral signals from low-dimensional device responses. We begin with the theory and geometry of linear estimation methods. These methods use linear models to characterize the likely input signals and reduce the number of estimation parameters. Next, we introduce two submanifold estimation methods. These methods are based on the observation that for many data sets the deviation between the signal and the linear estimate is systematic; the methods incorporate knowledge of these systematic deviations to improve upon linear estimation methods. We describe the geometric intuition of these methods and evaluate the submanifold method on hyperspectral image data.

© 2003 Optical Society of America

OCIS Codes
(330.1710) Vision, color, and visual optics : Color, measurement

History
Original Manuscript: September 30, 2002
Revised Manuscript: February 28, 2003
Manuscript Accepted: February 28, 2003
Published: July 1, 2003

Citation
Jeffrey M. DiCarlo and Brian A. Wandell, "Spectral estimation theory: beyond linear but before Bayesian," J. Opt. Soc. Am. A 20, 1261-1270 (2003)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-20-7-1261

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