Binary Tomography by Iterating Linear Programs from Noisy Projections
In this paper we improve the behavior of a reconstruction algorithm for binary tomography in the presence of noise. This algorithm which has recently been published is derived from a primal-dual subgradient method leading to a sequence of linear programs. The objective function contains a smoothness prior that favors spatially homogeneous solutions and a concave functional gradually enforcing binary solutions. We complement the objective function with a term to cope with noisy projections and evaluate its performance.
KeywordsDiscrete Tomography Combinatorial Optimization Linear Programming D.C. Programming Noise Suppression
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