Visa Korkiakoski, Christoph U. Keller, Niek Doelman, Rufus Fraanje, and Michel Verhaegen, "Joint optimization of phase diversity and adaptive optics: demonstration of potential," Appl. Opt. 51, 102-113 (2012)
We study different possibilities to use adaptive optics (AO) and phase diversity
(PD) together in a jointly optimized system. The potential of the joint system
is demonstrated through numerical simulations. We find that the most significant
benefits are obtained from the improved deconvolution of AO-corrected wavefronts
and the additional wavefront sensor (WFS) information that reduces the
computational demands of PD algorithms. When applied together, it is seen that
the image error can be reduced by 20% compared to traditional PD, working with
one focused and one defocused camera image, and the
computational load is reduced by a
factor of 20 compared to a more reliable PD algorithm requiring more camera
images. In addition, we find that the system performance can be optimized by
adjusting the magnitude of the
applied diversity wavefronts.
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PD applied to closed-loop WF residuals
WFS aid used in PD
PD reconstruction error () compared to original seeing-limited error ()
Relative error in reconstructed WF, ; only modal components considered
Fail rate, percentage of cases when
Fail rate, percentage of cases when , where denotes a momentary temporal average
Fail rate, percentage of cases when the condition f is fulfilled at least in one subimage at a given time-step
Number of FFTs for each considered subimage with a size of
Number of PD iterations for each considered subimage
PD applied to closed-loop WF residuals
WFS aid used in PD
PD reconstruction error () compared to original seeing-limited error ()
Relative error in reconstructed WF, ; only modal components considered
Fail rate, percentage of cases when
Fail rate, percentage of cases when , where denotes a momentary temporal average
Fail rate, percentage of cases when the condition f is fulfilled at least in one subimage at a given time-step
Number of FFTs for each considered subimage with a size of
Number of PD iterations for each considered subimage