A parametric estimation approach to instantaneous spectral imaging

Figen S. Oktem, Farzad Kamalabadi, Joseph M. Davila

Research output: Contribution to journalArticlepeer-review


Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging technique that estimates the physical parameters of interest by combining measurements with a parametric model and solving the resultant inverse problem computationally. The associated inverse problem, which can be viewed as a multiframe semiblind deblurring problem (with shift-variant blur), is formulated as a maximum a posteriori (MAP) estimation problem since in many such experiments prior statistical knowledge of the physical parameters can be well estimated. Subsequently, an efficient dynamic programming algorithm is developed to find the global optimum of the nonconvex MAP problem. Finally, the algorithm and the effectiveness of the spectral imaging technique are illustrated for an application in solar spectral imaging. Numerical simulation results indicate that the physical parameters can be estimated with the same order of accuracy as state-of-the-art slit spectroscopy but with the added benefit of an instantaneous, 2D field-of-view. This technique will be particularly useful for studying the spectra of dynamic scenes encountered in space remote sensing.

Original languageEnglish (US)
Article number6930780
Pages (from-to)5707-5721
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number12
StatePublished - Dec 1 2014


  • computational spectral imaging
  • dynamic programming
  • imaging spectroscopy
  • inverse methods
  • maximum posterior estimation
  • multiframe image deblurring
  • parameter estimation of superimposed signals
  • separable nonlinear least squares problems
  • space remote sensing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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