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Binary Tomography with Deblurring

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Combinatorial Image Analysis (IWCIA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4040))

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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.

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© 2006 Springer-Verlag Berlin Heidelberg

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Weber, S., Schüle, T., Kuba, A., Schnörr, C. (2006). Binary Tomography with Deblurring. In: Reulke, R., Eckardt, U., Flach, B., Knauer, U., Polthier, K. (eds) Combinatorial Image Analysis. IWCIA 2006. Lecture Notes in Computer Science, vol 4040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774938_30

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  • DOI: https://doi.org/10.1007/11774938_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35153-5

  • Online ISBN: 978-3-540-35154-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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