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18 - Image deconvolution

from Part IV - From detected photons to the celestial sphere

Published online by Cambridge University Press:  05 December 2012

Jorge Núñez
Affiliation:
Universitat de Barcelona and Obser vatorio Fabra
William F. van Altena
Affiliation:
Yale University, Connecticut
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Summary

Introduction

The techniques of image deconvolution can increase your effective telescope aperture by 40% without decreasing the astrometric precision or introducing artificial bias. Some studies also show that appreciable gain in astrometric accuracy can be obtained.

Theory of deconvolution

The imaging equation

In several parts of this book it has been pointed out that astrometry, as par t of astronomy, is an observational science in which the unknown physical basis is, in our observations, convolved with the structure of the source, the emission process, the atmosphere, the telescope detector interaction, etc. In a typical exposure of the sky taken from the ground this convolution makes our point-like stars appear as pixelized extended spots of light of about one arcsecond (or more) in size. The light in the star images shows, in general, a Gaussian-like pattern but it can vary across the frame. I n all types of observations (optical imaging from the ground or space, optical and radio interferometry, etc.), the process can be mathematically described as an imaging equation which is a relationship (with an integral operator) between the distribution of the source and the distribution of the observational data.

Type
Chapter
Information
Astrometry for Astrophysics
Methods, Models, and Applications
, pp. 265 - 276
Publisher: Cambridge University Press
Print publication year: 2012

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References

Fors, O. (2006). New observational techniques and analysis tools for wide field CCD surveys and high resolution astrometry. PhD Thesis. University of Barcelona. See: www.tdx.catGoogle Scholar
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Núñez, J. and Llacer, J. (1993). A general Bayesian image reconstruction algorithm with entropy prior. Preliminary application to HST data. PASP, 105, 1192.CrossRefGoogle Scholar
Núñez, J. and Llacer, J. (1998). Bayesian image reconstruction with space-variant noise suppression. A&AS, 131, 167.Google Scholar
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Prades, A. and Núñez, J. (1997). Improving astrometric measurements using image reconstruction. In Visual Double Stars. Formation, Dynamics and Evolutionary Tracks, ed. J. A., Dacobo, A., Elipe, and H., McAlister. Dordrecht: Kluwer Academic, p. 15.CrossRefGoogle Scholar
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  • Image deconvolution
  • Edited by William F. van Altena, Yale University, Connecticut
  • Book: Astrometry for Astrophysics
  • Online publication: 05 December 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139023443.019
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  • Image deconvolution
  • Edited by William F. van Altena, Yale University, Connecticut
  • Book: Astrometry for Astrophysics
  • Online publication: 05 December 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139023443.019
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Image deconvolution
  • Edited by William F. van Altena, Yale University, Connecticut
  • Book: Astrometry for Astrophysics
  • Online publication: 05 December 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139023443.019
Available formats
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