Accession Number : ADA261812


Title :   Bayesian Cross-Entropy Reconstruction of Complex Images


Descriptive Note : Final rept. 1 Jul 1991-30 Jun 1992


Corporate Author : ARIZONA UNIV TUCSON OPTICAL SCIENCES CENTER


Personal Author(s) : Frieden, B R ; Bajkova, Anisa T


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a261812.pdf


Report Date : 16 Nov 1992


Pagination or Media Count : 33


Abstract : Bajkova's 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.


Descriptors :   *IMAGES , *ENTROPY , ALGORITHMS , OUTPUT , FUNCTIONS , MODIFICATION , TWO DIMENSIONAL , EDGES , OPERATION , NOISE , APPROACH , FILTRATION , GRADIENTS , TRANSFORMATIONS , REJECTION , BIAS , BENEFITS , BAYES THEOREM , MAPS , WINDOWS , ONE DIMENSIONAL , PROBABILITY , SEQUENCES , QUALITY , SIGNALS


Subject Categories : Numerical Mathematics
      Thermodynamics


Distribution Statement : APPROVED FOR PUBLIC RELEASE