A Convex Programming Algorithm for Noisy Discrete Tomography

  • T.D. Capricelli
  • P.L. Combettes
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


A convex programming approach to discrete tomographic image reconstruction in noisy environments is proposed. Conventional constraints are mixed with noise-based constraints on the sinogram and a binariness-promoting total variation constraint. The noise-based constraints are modeled as confidence regions that are constructed under a Poisson noise assumption. A convex objective is then minimized over the resulting feasibility set via a parallel block-iterative method. Applications to binary tomographic reconstruction are demonstrated.


Image Recovery Poisson Noise Algebraic Reconstruction Technique Discrete Tomography Convex Feasibility Problem 
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

© Birkhäuser Boston 2007

Authors and Affiliations

  • T.D. Capricelli
    • 1
  • P.L. Combettes
    • 1
  1. 1.Lab. Jacques-Louis Lions - UMR 7598, Univ. Pierre et Marie Curie - Paris 6ParisFrance

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