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11 October 2010The impact of low signal-to-noise ratio values on the achievability of Cramér-Rao lower bounds with multi-frame blind deconvolution algorithms
Cramér-Rao lower bound (CRB) theory can be used to calculate algorithm-independent lower bounds to the variances of
parameter estimates. It is well known that the CRBs are achievable by algorithms only when the parameters can be
estimated with sufficiently-high signal-to-noise ratios (SNRs). Otherwise, the CRBs are still lower bounds, but there can
be a large gap between the CRBs and the variances that can be achieved by algorithms. We present results from our
initial investigations into the SNR dependence of the achievability of the CRBs by multi-frame blind deconvolution
(MFBD) algorithms for high-resolution imaging in the presence of atmospheric turbulence and sensor noise. With the
use of sample statistics, we give examples showing that the minimum SNR value for which the CRBs can be achieved
by our MFBD algorithm typically ranges between one and five, depending upon the strength of the prior knowledge used
in the algorithm and the SNRs in the measured data.
Charles L. Matson,Michael Flanagan, andR. Anthony Vincent
"The impact of low signal-to-noise ratio values on the achievability of Cramér-Rao lower bounds with multi-frame blind deconvolution algorithms", Proc. SPIE 7828, Optics in Atmospheric Propagation and Adaptive Systems XIII, 78280M (11 October 2010); https://doi.org/10.1117/12.864330
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Charles L. Matson, Michael Flanagan, R. Anthony Vincent, "The impact of low signal-to-noise ratio values on the achievability of Cramér-Rao lower bounds with multi-frame blind deconvolution algorithms," Proc. SPIE 7828, Optics in Atmospheric Propagation and Adaptive Systems XIII, 78280M (11 October 2010); https://doi.org/10.1117/12.864330