Reconstructing image differences from tomographic Poisson data

  • Joseph A. O'Sullivan
  • , Yaqi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Given two measurements of an image and a modified version of the image, we seek reconstructions of both the original image and the difference of the images. The data are assumed to be Poisson, with known nonnegative forward operator and nonnegative images. A penalized likelihood is minimized with the penalty equal to the sum of the absolute difference between the images. An alternating minimization algorithm is developed by reformulating the penalized maximum likelihood problem as a double minimization of I-divergence plus the penalty. This algorithm guarantees monotonic decrease in the objective function for each iteration. Simulations with random images and tomographic data are presented to demonstrate properties of the algorithm. Convergence properties of the algorithm are studied both theoretically and in simulations.

Original languageEnglish
Title of host publication2013 IEEE Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2013 - Proceedings
PublisherIEEE Computer Society
Pages124-129
Number of pages6
ISBN (Print)9781479916160
DOIs
StatePublished - 2013
Event2013 IEEE Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2013 - Napa, CA, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

Name2013 IEEE Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2013 - Proceedings

Conference

Conference2013 IEEE Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2013
Country/TerritoryUnited States
CityNapa, CA
Period08/11/1308/14/13

Keywords

  • alternating minimization algorithm
  • compressed sensing
  • image reconstruction
  • maximum likelihood estimation

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