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dc.contributor.authorAndrade-Loarca, Héctor
dc.contributor.authorKutyniok, Gitta Astrid Hildegard
dc.contributor.authorÖktem, Ozan
dc.contributor.authorPetersen, Philipp
dc.date.accessioned2022-08-25T11:01:49Z
dc.date.available2022-08-25T11:01:49Z
dc.date.issued2022-01-04
dc.description.abstractWe present a deep-learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging. The algorithm is based on a recently developed digital wavefront set extractor as well as the well-known microlocal canonical relation for the Radon transform. We use the wavefront set information about x-ray data to improve the reconstruction by requiring that the underlying neural networks simultaneously extract the correct ground truth wavefront set and ground truth image. As a necessary theoretical step, we identify the digital microlocal canonical relations for deep convolutional residual neural networks. We find strong numerical evidence for the effectiveness of this approach.en_US
dc.identifier.citationAndrade-Loarca, Kutyniok, Öktem, Petersen. Deep microlocal reconstruction for limited-angle tomography. Applied and Computational Harmonic Analysis. 2022;59:155-197en_US
dc.identifier.cristinIDFRIDAID 2022979
dc.identifier.doi10.1016/j.acha.2021.12.007
dc.identifier.issn1063-5203
dc.identifier.issn1096-603X
dc.identifier.urihttps://hdl.handle.net/10037/26407
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalApplied and Computational Harmonic Analysis
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleDeep microlocal reconstruction for limited-angle tomographyen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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