An approach to characterizing spatial aspects of image system blur

  • Jesse Adams
  • , Jessica Pillow
  • , Kevin Joyce
  • , Michael Brennan
  • , Malena I. Español
  • , Matthias Morzfeld
  • , Sean Breckling
  • , Daniel Champion
  • , Eric Clarkson
  • , Ryan Coffee
  • , Amanda Gehring
  • , Margaret Lund
  • , Duane Smalley
  • , Ajanae Williams
  • , Jacob Zier
  • , Daniel Frayer
  • , Marylesa Howard
  • , Eric Machorro

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Quantitative X-ray radiographic imaging systems that utilize a charged couple device (CCD) camera connected to a thick, monolithic scintillator can exhibit blur that varies spatially across the field of view, especially for thick scintillators used in pulse-power radiography of dynamically compressed objects. A three-point approach to estimating and accounting for this effect is demonstrated by (a) using a local estimation technique to measure the effect of blurring a calibration object at key locations across the field of view, (b) combining each of the local estimates into a spatially varying blurring function via partitions of unity interpolation, and (c) resolving the effects of that blur on the image by solving an ill-posed inverse problem using a spatially varying regularization term. The technique is demonstrated on synthetic examples and actual radiographs collected at the Naval Research Laboratory's (NRL) Mercury pulsed power facility.

Original languageEnglish (US)
Pages (from-to)583-595
Number of pages13
JournalStatistical Analysis and Data Mining
Volume14
Issue number6
DOIs
StatePublished - Dec 2021

Keywords

  • Bayesian
  • X-ray radiography
  • inverse problem
  • partition of unity
  • spatially varying blur

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

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