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Proceedings Article

Multiscale reconstruction for computational spectral imaging

[+] Author Affiliations
R. M. Willett, D. J. Brady

Duke Univ.

M. E. Gehm

Univ. of Arizona

Proc. SPIE 6498, Computational Imaging V, 64980L (February 28, 2007); doi:10.1117/12.715711
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From Conference Volume 6498

  • Computational Imaging V
  • Charles A. Bouman; Eric L. Miller; Ilya Pollak
  • San Jose, CA, USA | January 28, 2007

abstract

In this work we develop a spectral imaging system and associated reconstruction methods that have been designed to exploit the theory of compressive sensing. Recent work in this emerging field indicates that when the signal of interest is very sparse (i.e. zero-valued at most locations) or highly compressible in some basis, relatively few incoherent observations are necessary to reconstruct the most significant non-zero signal components. Conventionally, spectral imaging systems measure complete data cubes and are subject to performance limiting tradeoffs between spectral and spatial resolution. We achieve single-shot full 3D data cube estimates by using compressed sensing reconstruction methods to process observations collected using an innovative, real-time, dual-disperser spectral imager. The physical system contains a transmissive coding element located between a pair of matched dispersers, so that each pixel measurement is the coded projection of the spectrum in the corresponding spatial location in the spectral data cube. Using a novel multiscale representation of the spectral image data cube, we are able to accurately reconstruct 256×256×15 spectral image cubes using just 256×256 measurements.

© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

R. M. Willett ; M. E. Gehm and D. J. Brady
"Multiscale reconstruction for computational spectral imaging", Proc. SPIE 6498, Computational Imaging V, 64980L (February 28, 2007); doi:10.1117/12.715711; http://dx.doi.org/10.1117/12.715711


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