Maximum Bounded Entropy: Application to Tomographic Reconstruction.
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ARMY BALLISTIC RESEARCH LAB ABERDEEN PROVING GROUND MD
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We have investigated a new image restoring algorithm which utilizes maximum bounded entropy MBE. It incorporates prior knowledge of both a lower and upper bound of the signal in the unknown object. Its outputs are maximum probable estimates of the object, under the following conditions a the photons forming the image behave as classical particles b the object is assumed to be biased toward a flat, gray scene in the absence of image data c the object is modeled as consisting of high-gradient foreground details riding on top of a smoothly varying background that must be estimated in a separate step and d the image noise is Poisson. The proposed MBE estimator algorithm maximizes the sum of entropies of occupied photon sites. The result is an estimate of the object that is restricted to values inside the prescribed bounds. The algorithm was applied to the reconstruction of rod cross sections from tomographic viewing. In such a problem, the object consists only of upper-and lower-bound values, We found that in the example only four projections were needed to provide a good reconstruction, and that 20 projections allowed the partial resolution of a single pixel-wide crack in one of the rods. Originator-supplied keywords Entropy, Algorithms, Tomography, Image restoration, Computerized simulation.
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