BAYESIAN CROSS-ENTROPY RECONSTRUCTION OF COMPLEX IMAGES

Bajkova's generalized maximum entropy method for reconstruction of com plex signals is further generalized through the use of Kullback-Leible r 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 wi thin an overall maximum a posteriori probability approach that include s a noise-rejection term. A further modification is transformation of the large two-dimensional problem arising from modest-sized two-dimens ional images into a sequence of one-dimensional problems. Finally the added operation of three-point median window filtration of each interm ediary one-dimensional output is shown to suppress edge-top overshoots while augmenting edge gradients. Applications to simulated complex im ages are shown.