An algorithm for total variation regularized photoacoustic imaging

Yiqiu Dong, Torsten Görner, Stefan Kunis

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Recovery of image data from photoacoustic measurements asks for the inversion of the spherical mean value operator. In contrast to direct inversion methods for specific geometries, we consider a semismooth Newton scheme to solve a total variation regularized least squares problem. During the iteration, each matrix vector multiplication is realized in an efficient way using a recently proposed spectral discretization of the spherical mean value operator. All theoretical results are illustrated by numerical experiments.
Original languageEnglish
JournalAdvances in Computational Mathematics
Volume41
Issue number2
Pages (from-to)423-438
ISSN1019-7168
DOIs
Publication statusPublished - 2014

Keywords

  • Spherical mean operator
  • Fast Fourier transform
  • Total variation regularization
  • Photoacoustic imaging

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