Differential Zernike filter for phasing of segmented mirror and image processing
Abstract
The major objective of this thesis is to study the differential Zernike filter and its
applications in phasing segmented mirror and image processing. In terms of phasing, we
provide both theoretical analysis and simulation for a differential Zernike filter based
phasing technique, and find that the differential Zernike filter perform consistently
better than its counterpart, traditional Zernike filter. We also combine the differential
Zernike filter with a feedback loop, to represent a gradient-flow optimization dynamic
system. This system is shown to be capable of separating (static) misalignment errors of
segmented mirrors from (dynamical) atmospheric turbulence, and therefore compress
the effects of atmospheric turbulence. Except for segmented mirror phasing, we also
apply the Zernike feedback system in image processing. For the same system dynamics
as well as in segment phasing, the Zernike filter feedback system is capable of compress
the static noisy background, and makes the single particle tracking algorithm even
working in case of very low signal-to-noise ratio. Finally, we apply an efficient
multiple-particle tracking algorithm on a living cell image sequence. This algorithm is
shown to be able to deal with higher particle density, while the single particle tracking
methods are not working under this condition.