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Information Theoretic Bounds of Phase Diversity for Diversity Polynomials and Noise Statistics

机译:分集多项式和噪声统计的相位分集的信息理论界

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摘要

Information theoretic bounds on the estimated Zernike coefficients for various diversity phase functions are analyzed in this paper. We will show that, in certain cases, defocus diversity may yield higher Cramer-Rao lower bound (CRLB) than some other diversity phase functions. Evaluating the performance of the phase diversity algorithm using simulated images, we find that for an extended scene and defocus diversity, the phase diversity algorithm achieves the CRLB for known objects and approaches the CRLB by about a factor of two for unknown objects.
机译:本文分析了各种分集相位函数的估计Zernike系数的信息理论界。我们将证明,在某些情况下,散焦分集可能会比其他分集相位函数产生更高的Cramer-Rao下界(CRLB)。使用模拟图像评估相位分集算法的性能,我们发现,对于扩展场景和散焦分集,相位分集算法可实现已知对象的CRLB,而未知对象的CRLB约为其两倍。

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