dc.contributor.advisor | Munk, Axel Prof. Dr. | |
dc.contributor.author | Hobert, Anne | |
dc.date.accessioned | 2019-02-28T10:20:01Z | |
dc.date.available | 2020-01-28T23:50:02Z | |
dc.date.issued | 2019-02-28 | |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-002E-E5B3-9 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-7313 | |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.ddc | 510 | de |
dc.title | Semiparametric Estimation of Drift, Rotation and Scaling in Sparse Sequential Dynamic Imaging: Asymptotic theory and an application in nanoscale fluorescence microscopy | de |
dc.type | doctoralThesis | de |
dc.contributor.referee | Munk, Axel Prof. Dr. | |
dc.date.examination | 2019-01-29 | |
dc.description.abstracteng | Light microscopy is an important instrument in life sciences. Over the last two decades,
superresolution fluorescence microscopy techniques have been established, breaking the Abbé
diffraction barrier, which before had posed a resolution limitation for over a century. The
fundamentally new idea of these approaches is to use optically switchable fluorophores in order
to detect features within the resolution limit imposed by the diffraction barrier consecutively
instead of simultaneously. However, the relatively long imaging times needed in many modern
superresolution fluorescence microscopy techniques at the nanoscale, one of them being single
marker switching (SMS) microscopy, come with their own drawbacks. The challenge lies in
the correct alignment of long sequences of sparse but spatially and temporally highly resolved
images. This alignment is necessary due to rigid motion of the displayed object of interest
or its supporting area during the observation process. In this thesis, a semiparametric model
for motion correction, including drift, rotation and scaling of the imaged specimen, is used to
estimate the motion and correct for it, reconstructing thereby the true underlying structure of
interest. This technique is also applicable in many other scenarios, where an aggregation of
a collection of sparse images is employed to obtain a good reconstruction of the underlying
structure, like, for example, in real time magnetic resonance imaging (MRI). | de |
dc.contributor.coReferee | Krivobokova, Tatyana Prof. Dr. | |
dc.subject.eng | SMS microscopy | de |
dc.subject.eng | SML microscopy | de |
dc.subject.eng | Mathematical statistics | de |
dc.subject.eng | Asymptotic theory | de |
dc.subject.eng | Nanoscale fluorescence microscopy | de |
dc.subject.eng | Central limit theorem | de |
dc.subject.eng | M-estimators | de |
dc.subject.eng | Motion estimation | de |
dc.identifier.urn | urn:nbn:de:gbv:7-11858/00-1735-0000-002E-E5B3-9-2 | |
dc.affiliation.institute | Fakultät für Mathematik und Informatik | de |
dc.subject.gokfull | Mathematik (PPN61756535X) | de |
dc.description.embargoed | 2020-01-28 | |
dc.identifier.ppn | 106735199X | |