Abstract
Classical techniques for the reconstruction of axisymmetrical objects are all creating artefacts (smooth or unstable solutions). Moreover, the extraction of very precise features related to big density transitions remains quite delicate. In this paper, we develop a new approach -in one dimension for the moment- that allows us both to reconstruct and to extract characteristics: an a priori is provided thanks to a density model. We show the interest of this method in regard to noise effects quantification ; we also explain how to take into account some physical perturbations occuring with real data acquisition.
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Lagrange, J.M., Abraham, I. (2004). Model-Based Approach to Tomographic Reconstruction Including Projection Deblurring. Sensitivity of Parameter Model to Noise on Data. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24673-2_4
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DOI: https://doi.org/10.1007/978-3-540-24673-2_4
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