Multiscale reconstruction for computational spectral imaging

R. M. Willett, M. E. Gehm, D. J. Brady

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

45 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Computational Imaging V
StatePublished - 2007
Externally publishedYes
EventComputational Imaging V - San Jose, CA, United States
Duration: Jan 29 2007Jan 31 2007

Publication series

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


ConferenceComputational Imaging V
Country/TerritoryUnited States
CitySan Jose, CA


  • Compressed sensing
  • Hyperspectral imaging
  • Wavelets

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Multiscale reconstruction for computational spectral imaging'. Together they form a unique fingerprint.

Cite this