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A Cramer-Rao Lower Bound Analysis Of Noise Reduction Limits In Blind Deconvolution For Zernike-Based Point- Spread-Function Estimation With The Use Of A Support Constraint

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Abstract

We show in an algorithm-independent way that Zemike-based blind deconvolution of atmospherically-blurred images produces higher-quality estimates of an object and the blurring PSFs than does pixel-based PSF estimation.

© 2006 Optical Society of America

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