Abstract

Linear estimation theory is developed in the context of object reconstruction from data obtained by a general shift-variant imaging system. The formalism adopts nonstationary first- and second-order statistics of the object and noise classes as a priori information. In addition, a metric for system optimization that depends on the a priori information is presented. The role of this a priori information as derived from several different training sets is then studied with respect to reconstruction performance for various noise levels in the data, using a tomographic coded-aperture system as the model. In a separate experiment, a simple coded-aperture system is optimized to a particular object class, and the results are compared with those from an earlier optimization experiment.

© 1988 Optical Society of America

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