An approach to characterizing spatial aspects of image system blur. (25th February 2021)

• Record Type: Journal Article
• Authors: Adams, Jesse; Pillow, Jessica; Joyce, Kevin; Brennan, Michael; Español, Malena I.; Morzfeld, Matthias; Breckling, Sean; Champion, Daniel; Clarkson, Eric; Coffee, Ryan; Gehring, Amanda; Lund, Margaret; Smalley, Duane; Williams, Ajanae; Zier, Jacob; Frayer, Daniel; Howard, Marylesa; Machorro, Eric
• Other Names: Morris Max D. guestEditor.
• Is Part Of: Statistical analysis and data mining. Volume 14:Number 6(2021)
• Page Start: 583
• Page End: 595
• Publication Date: 2021-02-25
• Subjects: Bayesian -- inverse problem -- partition of unity -- spatially varying blur -- X‐ray radiography; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11501
• Languages: English
• ISSNs: 1932-1864

Abstract

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.