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DGCI
2006
Springer

A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography

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A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography
Abstract. Discrete tomography concerns the reconstruction of functions with a finite number of values from few projections. For a number of important real-world problems, this tomography problem involves thousands of variables. Applicability and performance of discrete tomography therefore largely depend on the criteria used for reconstruction and the optimization algorithm applied. From this viewpoint, we evaluate two major optimization strategies, simulated annealing and convex-concave regularization, for the case of binary-valued functions using various data sets. Extensive numerical experiments show that despite being quite different from the viewpoint of optimization, both strategies show similar reconstruction performance as well as robustness to noise.
Stefan Weber, Antal Nagy, Thomas Schüle, Chri
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DGCI
Authors Stefan Weber, Antal Nagy, Thomas Schüle, Christoph Schnörr, Attila Kuba
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