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dc.contributor.authorZang, Guangming
dc.contributor.authorAly, Mohamed
dc.contributor.authorIdoughi, Ramzi
dc.contributor.authorWonka, Peter
dc.contributor.authorHeidrich, Wolfgang
dc.date.accessioned2018-10-08T11:38:52Z
dc.date.available2018-10-08T11:38:52Z
dc.date.issued2018-10-06
dc.identifier.citationZang, G., Aly, M., Idoughi, R., Wonka, P., & Heidrich, W. (2018). Super-Resolution and Sparse View CT Reconstruction. Lecture Notes in Computer Science, 145–161. doi:10.1007/978-3-030-01270-0_9
dc.identifier.doi10.1007/978-3-030-01270-0_9
dc.identifier.urihttp://hdl.handle.net/10754/628903
dc.description.abstractWe present a flexible framework for robust computed tomography (CT) reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator. We show empirically that CG often does not converge to the global optimum for tomography problem even though the underlying problem is convex. We demonstrate that using SART provides better reconstruction results in sparse-view settings using fewer projection images. We provide extensive experimental results for both contributions on both simulated and real data. Moreover, our code will also be made publicly available.
dc.description.sponsorshipThis work was supported by KAUST as part of VCC Center Competitive Funding
dc.relation.urlhttps://vccimaging.org/Publications/Zang2018SuperResolutionCT/
dc.subjectSuper resolution
dc.subjectProximal optimization
dc.subjectTomography
dc.titleSuper-Resolution and Sparse View CT Reconstruction
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateSeptember 8 - 14 2018
dc.conference.nameEuropean Conference on Computer Vision (ECCV)
dc.conference.locationMunich, Germany
dc.eprint.versionPost-print
refterms.dateFOA2018-10-08T00:00:00Z
dc.date.published-online2018-10-06
dc.date.published-print2018


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