Bayesian Cross-Entropy Reconstruction of Complex Images
Final rept. 1 Jul 1991-30 Jun 1992
ARIZONA UNIV TUCSON OPTICAL SCIENCES CENTER
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Bajkovas generalized maximum entropy method GMEM for reconstruction of complex signals has been further generalized through the use of Kullback-Leibler cross entropy. This permits a priori information in the form of bias functions to be inserted into the algorithm, with resulting benefits to reconstruction quality. Also, the cross-entropy term is imbedded within an overall m.a.p. maximum a posteriori probability approach that includes a noise-rejection term. A further modification is transformation of the large, two-dimensional problem due to modest-sized 2-D images into a sequence of one- dimensional problems. Finally, the added operation of three-point median window filtration of each intermediary, one-dimensional output is shown to suppress edge-top overshoots while augmenting edge gradients. Applications to simulated complex images are shown.
- Numerical Mathematics