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Proceedings Paper

Bayesian image reconstruction from partial image and spectral amplitude data
Author(s): Shyamsunder Baskaran; Rick P. Millane
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Paper Abstract

We address a problem of reconstruction of a periodic image from information in image space and Fourier space. The real space information consists of knowledge of part of the image, while the Fourier space information is data in the form of sums of the squares of the amplitudes of sets of particular Fourier coefficients of the image. Such a problem occurs in the determination of polymer structures from x-ray fiber diffraction data. We present a Bayesian approach to this problem, incorporating a priori model for the image based on the structure being made up of 'atoms'. The Bayesian minimum mean-square-error estimate for the missing part of the image is derived. Currently used heuristic estimates are the maxima of certain posterior densities. Simulations are performed for varying amounts of real and Fourier space information to assess the performance of the different estimators. The performance of the minimum mean- square-error estimate is superior to that of the other estimates.

Paper Details

Date Published: 31 October 1997
PDF: 11 pages
Proc. SPIE 3170, Image Reconstruction and Restoration II, (31 October 1997); doi: 10.1117/12.292824
Show Author Affiliations
Shyamsunder Baskaran, Purdue Univ. (United States)
Rick P. Millane, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 3170:
Image Reconstruction and Restoration II
Timothy J. Schulz, Editor(s)

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