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- W20222495 abstract "Abstract We present an explicit mechanical image model for Bayesian reconstruction from tomographic data. The image intensity of each pixel is modeled by a transverse motion of a so-called “pixtron” with unknown mass. A prior energy for Bayesian tomographic reconstruction is therefore interpreted as the total kinetic energy of a collection of pixtrons. With the log-likelihood as the potential energy restricting the motion of pixtrons, the minimization of a log-posterior is an analogue of the principle of least action in the classical mechanics. We show that the Gaussian Markov random field prior can be viewed as the kinetic energy of free motion of pixtrons. With the framework of the mechanical image model, we propose a novel image prior for Bayesian tomographic reconstruction based on level-set evolution of an image driven by the mean curvature motion. As it has been studied in image processing with nonlinear diffusion, this prior encourages the stabilization of an edge while the reconstructed image is smoothed along both sides of the edge. A distinguished feature of our approach is that the curvature term itself appears in the image prior rather than in the resulting differential equation derived from the total variation method. An algorithm of iterated coordinate descent has been implemented with the proposed prior using Brent's method for one-dimensional optimization. Our simulation results demonstrate that our algorithm can outperform existing priors for preserving sharp edges during tomographic reconstruction without introducing additional artifacts." @default.
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- W20222495 date "2003-01-01" @default.
- W20222495 modified "2023-09-27" @default.
- W20222495 title "A Mechanical Image Model for Bayesian Tomographic Reconstruction" @default.
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- W20222495 doi "https://doi.org/10.1016/s1570-579x(03)80034-4" @default.
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