1,432 research outputs found

    Reconstruction from Radon projections and orthogonal expansion on a ball

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    The relation between Radon transform and orthogonal expansions of a function on the unit ball in \RR^d is exploited. A compact formula for the partial sums of the expansion is given in terms of the Radon transform, which leads to algorithms for image reconstruction from Radon data. The relation between orthogonal expansion and the singular value decomposition of the Radon transform is also exploited.Comment: 15 page

    Approximation and Reconstruction from Attenuated Radon Projections

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    Attenuated Radon projections with respect to the weight function Wμ(x,y)=(1−x2−y2)μ−1/2W_\mu(x,y) = (1-x^2-y^2)^{\mu-1/2} are shown to be closely related to the orthogonal expansion in two variables with respect to WμW_\mu. This leads to an algorithm for reconstructing two dimensional functions (images) from attenuated Radon projections. Similar results are established for reconstructing functions on the sphere from projections described by integrals over circles on the sphere, and for reconstructing functions on the three-dimensional ball and cylinder domains.Comment: 25 pages, 3 figure

    A new approach to the reconstruction of images from Radon projections

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    A new approach is proposed for reconstruction of images from Radon projections. Based on Fourier expansions in orthogonal polynomials of two and three variables, instead of Fourier transforms, the approach provides a new algorithm for the computed tomography. The convergence of the algorithm is established under mild assumptions.Comment: 28 pages, accepted by Adv. in Applied Mat

    Inversion of noisy Radon transform by SVD based needlet

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    A linear method for inverting noisy observations of the Radon transform is developed based on decomposition systems (needlets) with rapidly decaying elements induced by the Radon transform SVD basis. Upper bounds of the risk of the estimator are established in LpL^p (1≤p≤∞1\le p\le \infty) norms for functions with Besov space smoothness. A practical implementation of the method is given and several examples are discussed
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