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Phase retrieval problems in x-ray physics

dc.contributor.authorHomann, Carolin
dc.date.accessioned2015-06-26T08:49:22Z
dc.date.available2015-06-26T08:49:22Z
dc.date.issued2015
dc.identifier.urihttps://doi.org/10.17875/gup2015-816
dc.format.extentII, 123
dc.format.mediumPrint
dc.language.isoeng
dc.relation.ispartofseriesGöttingen Series in X-ray Physics
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.subject.ddc530
dc.titlePhase retrieval problems in x-ray physics
dc.title.alternativeFrom modeling to efficient algorithms
dc.typemonograph
dc.price.print32,00
dc.identifier.urnurn:nbn:de:gbv:7-univerlag-isbn-978-3-86395-210-5-3
dc.identifier.ppn828542759
dc.relation.ppn828541531
dc.description.printSoftcover, 17x24
dc.subject.divisionpeerReviewed
dc.subject.subjectheadingPhysik
dc.relation.isbn-13978-3-86395-210-5
dc.relation.issn2191-9860
dc.identifier.articlenumber8101423
dc.identifier.internisbn-978-3-86395-210-5
dc.bibliographicCitation.volume016
dc.type.subtypethesis
dc.subject.bisacSCI055000
dc.notes.oaiprint
dc.subject.vlb640
dc.subject.bicPH
dc.description.abstractengIn phase retrieval problems that occur in imaging by coherent x-ray diffraction, one tries to reconstruct information about a sample of interest from possibly noisy intensity measurements of the wave fi eld traversing the sample. The mathematical formulation of these problems bases on some assumptions. Usually one of them is that the x-ray wave fi eld is generated by a point source. In order to address this very idealized assumption, it is common to perform a data preprocessing step, the so-called empty beam correction. Within this work, we study the validity of this approach by presenting a quantitative error estimate. Moreover, in order to solve these phase retrieval problems, we want to incorporate a priori knowledge about the structure of the noise and the solution into the reconstruction process. For this reason, the application of a problem adapted iteratively regularized Newton-type method becomes particularly attractive. This method includes the solution of a convex minimization problem in each iteration step. We present a method for solving general optimization problems of this form. Our method is a generalization of a commonly used algorithm which makes it efficiently applicable to a wide class of problems. We also proof convergence results and show the performance of our method by numerical examples.
dc.notes.vlb-printlieferbar
dc.intern.doi10.17875/gup2015-816
dc.identifier.purlhttp://resolver.sub.uni-goettingen.de/purl?univerlag-isbn-978-3-86395-210-5
dc.identifier.asin3863952103
dc.subject.themaPH


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