Paper Info
Title
A Linear Programming Relaxation for Binary Tomography with Smoothness Priors
Abstract
We focus on the reconstruction of binary functions from a small number of X-ray projections. The linear-programming (LP) relaxation to this combinatorial optimization problem due to Fishburn et al. is extended to objective functionals with quadratic smoothness priors. We show that the regularized LP-relaxation provides a good approximation and thus allows to bias the reconstruction towards solutions with spatially coherent regions. These solutions can be computed with any interior-point solver and a related rounding technique. Our approach provides an alternative to computationally expensive MCMC-sampling (Markov Chain Monte Carlo) techniques and other heuristic rounding schemes.
Year
DOI
Venue
2003
10.1016/S1571-0653(04)00490-1
Electronic Notes in Discrete Mathematics
Keywords
DocType
Volume
Discrete Tomography,Markov Random Fields,Combinatorial Optimization,LP-Relaxation,Approximation Algorithm,Regularization
Journal
12
ISSN
Citations 
PageRank 
1571-0653
21
1.75
References 
Authors
7
3
Name
Order
Citations
PageRank
S Weber113310.09
Christoph Schnörr23025219.34
Joachim Hornegger316817.44