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

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)
JournalStatistical Analysis and Data Mining
DOIs
StateAccepted/In press - 2021
Externally publishedYes

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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