Abstract

A new procedure is described for reconstructing one-dimensional discrete finite objects. The procedure uses both long-exposure and short-exposure images rather than only long-exposure or short-exposure images. It reconstructs the minimum-phase part of the object in two steps. First, it estimates the true autocorrelation function of the object by using the noisy correlation data that can be derived from the short-exposure images. Next, it invokes the fast Levinson or Schur recursions of one-dimensional linear least-squares prediction to estimate the minimum-phase part of the object from the estimated object correlation data. The procedure reconstructs the all-pass part of the object by initially estimating the number of factors in that part by using the noisy long-exposure data. A least-squares approach is then used to find those factors. The object is finally reconstructed by combining the estimates of its minimum-phase and all-pass factors.

© 1990 Optical Society of America

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