Topics in Unconventional Imagery

Abstract

Two problems in unconventional imagery, were worked on, (a) an exact solution to the in age turbulence problem (also called the 'blind deconvolution' problem); and (b) closed-form maximum entropy (M.E.) image restoration Progress on (a) was as follows. It was found that by dividing the image spectra of two short-exposure images of an incoherent object viewed through random turbulence, a system of linear equations can be generated. The unknowns of the equations are the sampled values of the two point spread functions characterizing the two images. These can be found, with arbitrary precision, by simple inversion of the equations. Then the object is restored by inverse filtering the two images with transfer functions generated from the known point spread functions. The approach works perfectly in the absence of additional randomness due to noise of detection, and tolerates small amounts of such noise. Progress on (b) was as follows. Doctoral student David Graser tested out the closed-form M.E. approach by computer simulation. Two widely-used classes of test objects--point sources and edge sources--were used as inputs, and these were imaged using Gaussian spread functions of given halfwidths. The M.E. outputs were found to be, overall, superior to corresponding outputs using clipped inverse-filtering and Wiener filtering.

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Document Details

Document Type
Technical Report
Publication Date
Nov 04, 1997
Accession Number
ADA331691

Entities

People

  • B. R. Frieden
  • David J. Graser

Organizations

  • University of Arizona

Tags

Communities of Interest

  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Atmospheric Motion
  • Clear Air Turbulence
  • Detection
  • Digital Image Processing
  • Digital Images
  • Equations
  • Filtration
  • Image Processing
  • Image Reconstruction
  • Image Restoration
  • Kernel Functions
  • Lead Time
  • Spectra
  • Transfer Functions
  • Turbulence
  • Wind
  • Wind Shear

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.