Abstract
Binary tomography focuses on the problem of reconstructing homogeneous objects from a small number of their projections. In many applications, incomplete projection data holds insufficient information for the correct reconstruction of the original object. In this paper, we provide an optimization based method to select the “most informative” projection set, using information of global uncertainty. Beside the projection data we assume no further knowledge of the image to be reconstructed. Still, we achieve approximately as accurate reconstruction results, as it is possible to gain with a former method that uses blueprint images to find the optimal set of projections. We give experimental results for validating our approach on artificial images of various structures.
This research was supported by the NKFIH OTKA [grant number K112998] and by the project “Integrated program for training new generation of scientists in the fields of computer science”, no EFOP-3.6.3-VEKOP-16-2017-0002. The project has been supported by the European Union and co-funded by the European Social Fund.
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Lékó, G., Balázs, P., Varga, L.G. (2018). Projection Selection for Binary Tomographic Reconstruction Using Global Uncertainty. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_1
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DOI: https://doi.org/10.1007/978-3-319-93000-8_1
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