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An approach to characterizing spatial aspects of image system blur

Journal Article · · Statistical Analysis and Data Mining
DOI:https://doi.org/10.1002/sam.11501· OSTI ID:1777178
 [1];  [2];  [3];  [4];  [5];  [6];  [1];  [1];  [7];  [8];  [9];  [1];  [1];  [1];  [10];  [1];  [1];  [11]
  1. Nevada National Security Site (NNSS), Las Vegas, NV (United States)
  2. Nevada National Security Site (NNSS), Las Vegas, NV (United States); Univ. of Arizona, Tucson, AZ (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  4. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  5. Arizona State Univ., Tempe, AZ (United States)
  6. Univ. of California, San Diego, CA (United States)
  7. Univ. of Arizona, Tucson, AZ (United States)
  8. SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS)
  9. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  10. Naval Research Lab. (NRL), Washington, DC (United States)
  11. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
+ Show Author Affiliations

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. Here, 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.

Research Organization:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Nevada National Security Site/Mission Support and Test Services LLC; Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344; 89233218CNA000001; AC02-76SF00515; AC05-76RL01830; NA0003624
OSTI ID:
1777178
Alternate ID(s):
OSTI ID: 1784700
OSTI ID: 1786602
Report Number(s):
DOE/NV/03624--0791; LA-UR--20-24323
Journal Information:
Statistical Analysis and Data Mining, Journal Name: Statistical Analysis and Data Mining Journal Issue: 6 Vol. 14; ISSN 1932-1864
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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97 MATHEMATICS AND COMPUTING
Bayesian
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inverse problem
partition of unity
spatially varying blur