Blind Deconvolution of HST Simulated Data

Julian C. Christou Stuart M. Jefferies Mark W. Robison


We apply an iterative deconvolution algorithm, which has the capability to recover both the object and point spread function from a single image or multiple images, to simulated HST star cluster data. The algorithm uses error metric minimization to enforce known physical constraints on both the reconstructed object and point spread function. The reconstructed object is shown to preserve the photometry inherent in the observed image. The use of multiple observations improves the signal-to-noise ratio of the reconstructed


Pages 212-218


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