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Reconstruction of a gray level image from the autocorrelation function with insufficient or noisy constraint

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Abstract

For optical pattern recognition or triple correlation use, it is often necessary to obtain an improved image of the input to attain strong correlation. One way to do this is to perform associative retrieval using the incomplete data into an associative memory. The other possibility is to retrieve data from the autocorrelation function result with some approximate constraint. The problem then becomes a phase retrieval problem used for remote sensing, wavefront sensing or crystallography. Simulation results demonstrating effects of incompleteness of the data constraint with the rate of convergence is illustrated. Possible use in building or enhancing the associative recall process is explored.

© 1991 Optical Society of America

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