Discrete tomography by convex-concave regularization and D.C. programming
Schüle T, Schnörr C, Weber S, Hornegger J (2005)
Publication Type: Journal article, Original article
Publication year: 2005
Journal
Original Authors: Schüle T., Schnörr C., Weber S., Hornegger J.
Publisher: Elsevier
Book Volume: 151
Pages Range: 229-243
Journal Issue: null
DOI: 10.1016/j.dam.2005.02.028
Abstract
We present a novel approach to the tomographic reconstruction of binary objects from few projection directions within a limited range of angles. A quadratic objective functional over binary variables comprising the squared projection error and a prior penalizing non-homogeneous regions, is supplemented with a concave functional enforcing binary solutions. Application of a primal-dual subgradient algorithm to a suitable decomposition of the objective functional into the difference of two convex functions leads to an algorithm which provably converges with parallel updates to binary solutions. Numerical results demonstrate robustness against local minima and excellent reconstruction performance using five projections within a range of 90°. Our approach is applicable to quite general objective functions over binary variables with constraints and thus applicable to a wide range of problems within and beyond the field of discrete tomography. © 2005 Elsevier B.V. All rights reserved.
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Germany (DE)How to cite
APA:
Schüle, T., Schnörr, C., Weber, S., & Hornegger, J. (2005). Discrete tomography by convex-concave regularization and D.C. programming. Discrete Applied Mathematics, 151(null), 229-243. https://dx.doi.org/10.1016/j.dam.2005.02.028
MLA:
Schüle, Thomas, et al. "Discrete tomography by convex-concave regularization and D.C. programming." Discrete Applied Mathematics 151.null (2005): 229-243.
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