Abstract

A blind deconvolution algorithm based on the Richardson–Lucy deconvolution algorithm is presented. Its performance in the presence of noise is found to be superior to that of other blind deconvolution algorithms. Results are presented and compared with results obtained from implementation of a Weiner filter blind deconvolution algorithm. The algorithm is developed further to incorporate functional forms of the point-spread function with unknown parameters. In the presence of noise the point-spread function can be evaluated with 1.0% error, and the object can be reconstructed with a quality near that of the deconvolution process with a known point-spread function.

© 1995 Optical Society of America

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