Abstract
We study two scenarios of limited-angle binary tomography with data distorted with an unknown convolution: Either the projection data are taken from a blurred object, or the projection data themselves are blurred. These scenarios are relevant in case of scattering and due to a finite resolution of the detectors. Assuming that the unknown blurring process is adequately modeled by an isotropic Gaussian convolution kernel with unknown scale-parameter, we show that parameter estimation can be combined with the reconstruction process. To this end, a recently introduced Difference-of-Convex-Functions programming approach to limited-angle binary tomographic reconstruction is complemented with Expectation-Maximization iteration. Experimental results show that the resulting approach is able to cope with both ill-posed problems, limited-angle reconstruction and deblurring, simultaneously.
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References
Gonzales, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison Wesley, Reading (1992)
Herman, G.T., Kuba, A. (eds.): Discrete Tomography: Foundations, Algorithms, and Applications. Birkhäuser, Boston (1999)
Balaskó, M., Kuba, A., Nagy, A., Kiss, Z., Rodek, L., Ruskó, L.: Neutron-, gamma-, and X-ray three-dimensional computed tomography at the Budapest research reactor site. Nuclear Instruments and Methods in Physics Research A 542, 22–27 (2005)
Kuba, A., Ruskó, L., Rodek, L., Kiss, Z.: Preliminary sudies of discrete tomography in neutron imaging. IEEE Trans. Nucl. Sci. NS-52, 380–385 (2005)
Carazo, J.M., Sorzano, C.O., Rietzel, E., Schröder, R., Marabini, R.: Discrete tomography in electron microscopy. In: Herman, G.T., Kuba, A. (eds.) Discrete Tomography. Foundations, Algorithms, and Applications, pp. 405–416. Birkhäuser, Boston (1999)
Schüle, T., Schnörr, C., Weber, S., Hornegger, J.: Discrete tomography by convex-concave regularization and d.c. programming. Discrete Applied Mathematics 151, 229–243 (2005)
Weber, S., Schüle, T., Schnörr, C., Hornegger, J.: A linear programming approach to limited angle 3d reconstruction from dsa projections. Special Issue of Methods of Information in Medicine 4, 320–326 (2004)
Schnörr, C., Schüle, T., Weber, S.: Variational Reconstruction with DC-Programming. In: Herman, G.T., Kuba, A. (eds.) Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston (to appear, 2006)
MacLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley, Chichester (1996)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Capricelli, T., Combettes, P.: Parallel block-iterative reconstruction algorithms for binary tomography. Electr. Notes in Discr. Math. 20, 263–280 (2005)
Pham Dinh, T., Elbernoussi, S.: Duality in d.c. (difference of convex functions) optimization subgradient methods. In: Trends in Mathematical Optimization. Int. Series of Numer. Math., vol. 84, pp. 277–293. Birkhäuser Verlag, Basel (1988)
Pham Dinh, T., Hoai An, L.T.: A d.c. optimization algorithm for solving the trust-region subproblem. SIAM J. Optim. 8(2), 476–505 (1998)
Rockafellar, R.T.: Convex analysis, 2nd edn. Princeton Univ. Press, Princeton (1972)
Birgin, E.G., Martínez, J.M., Raydan, M.: Algorithm 813: SPG - software for convex-constrained optimization. ACM Transactions on Mathematical Software 27, 340–349 (2001)
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Weber, S., Schüle, T., Kuba, A., Schnörr, C. (2006). Binary Tomography with Deblurring. In: Reulke, R., Eckardt, U., Flach, B., Knauer, U., Polthier, K. (eds) Combinatorial Image Analysis. IWCIA 2006. Lecture Notes in Computer Science, vol 4040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774938_30
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DOI: https://doi.org/10.1007/11774938_30
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