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Binary Tomography with Deblurring

  • Stefan Weber
  • Thomas Schüle
  • Attila Kuba
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4040)

Abstract

We study two scenarios of limited-angle binary tomography with data distorted with an unknown convolution: Either the projection data are taken from a blurred object, or the projection data themselves are blurred. These scenarios are relevant in case of scattering and due to a finite resolution of the detectors. Assuming that the unknown blurring process is adequately modeled by an isotropic Gaussian convolution kernel with unknown scale-parameter, we show that parameter estimation can be combined with the reconstruction process. To this end, a recently introduced Difference-of-Convex-Functions programming approach to limited-angle binary tomographic reconstruction is complemented with Expectation-Maximization iteration. Experimental results show that the resulting approach is able to cope with both ill-posed problems, limited-angle reconstruction and deblurring, simultaneously.

Keywords

Binary Tomography Reconstruction Algorithm Gaussian Kernel Projection Data Reconstruction Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Weber
    • 1
  • Thomas Schüle
    • 1
    • 3
  • Attila Kuba
    • 2
  • Christoph Schnörr
    • 1
  1. 1.Dept. of Mathematics and Computer Science, CVGPR-GroupUniversity of MannheimMannheimGermany
  2. 2.Dept. of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary
  3. 3.Siemens Medical SolutionsForchheimGermany

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