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Image correlation techniques, such as bispectrurn analysis, are now widely used in astronomy and other fields to obtain high resolution images. We report on two new methods for obtaining such images from intensity correlation functions. Both methods treat the correlation functions as convolutions and attempt to deconvolve these functions while using a priori information to constrain the results. The first method is an iterative algorithm based on deconvolution schemes recently proposed by us1,2. The algorithm constrains any reconstructed image to be positive and forces its associated spatial correlation spectrum to be consistent, within noise estimates, with the spectrum of the measured correlation function. The second method is an implementation of the Monte-Carlo optimization technique known as simulated annealing. This method minimizes the difference between the measured correlation spectrum and that associated with the deconvolved image. The two proposed methods are describe and results are presented for both computationaly simulated data and astronomical data obtained at the Wyoming Infrared Observatory.
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