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Estimating rigid motion in sparse sequential dynamic imaging: with application to nanoscale fluorescence microscopy

dc.contributor.advisorMunk, Axel Prof. Dr.
dc.contributor.authorHartmann, Alexander
dc.date.accessioned2017-04-05T09:36:10Z
dc.date.available2017-04-05T09:36:10Z
dc.date.issued2017-04-05
dc.identifier.urihttp://hdl.handle.net/11858/00-1735-0000-0023-3E08-0
dc.identifier.urihttp://dx.doi.org/10.53846/goediss-6237
dc.language.isoengde
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510de
dc.titleEstimating rigid motion in sparse sequential dynamic imaging: with application to nanoscale fluorescence microscopyde
dc.typedoctoralThesisde
dc.contributor.refereeMunk, Axel Prof. Dr.
dc.date.examination2016-04-22
dc.description.abstractengIn this work, we deal with sequences of pixel images (frames) which are noisy shifted, rotated, and scaled versions of some unknown image f. Moreover, those frames are sparse in the sense that they do not show the whole transformed (and noisy) image f but only relatively few pixels (at random locations). If the sequence contains enough frames, it is likely that every pixel is observed in at least one of them, and summing up all frames yields a rather complete version of the unknown image. However, since the single frames are subject to rigid motions, the result is blurred. This situation comes up in single marker switching (SMS) microscopy. In applications, the frames are often calibrated by tracking the positions of so-called fiducial markers (bright spots that are fixed to the specimen and appear in every frame). This method is technically demanding and has further drawbacks. We propose a purely statistical reconstruction method based on parametric models for the drift, rotation, and scaling functions, where we estimate those parameters by minimizing certain functionals. We prove consistency of our M-estimators, asymptotic normality of the rotation and scaling parameter estimators, and uniform tightness of the drift parameter estimator. Furthermore, we test our M-estimators in a simulation study with various parametric motion models and statistical error models. Last but not least, we apply our method to SMS microscopy data and construct bootstrap confidence bands for the drift, rotation, and scaling functions.de
dc.contributor.coRefereeHuckemann, Stephan Prof. Dr.
dc.subject.engmotion estimationde
dc.subject.engimage registrationde
dc.subject.engsemiparametricsde
dc.subject.engM-estimationde
dc.subject.engnanoscale fluorescence microscopyde
dc.subject.engsuper resolution microscopyde
dc.subject.engasymptotic normalityde
dc.subject.engsparsityde
dc.subject.engregistrationde
dc.identifier.urnurn:nbn:de:gbv:7-11858/00-1735-0000-0023-3E08-0-1
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullMathematics (PPN61756535X)de
dc.identifier.ppn883925036


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