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DC poleHodnotaJazyk
dc.contributor.authorErzar, Blaž
dc.contributor.authorLesar, Žiga
dc.contributor.authorMarolt, Matija
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-17T15:32:52Z
dc.date.available2023-10-17T15:32:52Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 204-212.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54426
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectrozpoznávání obrazucs
dc.subjectnumerická interpolacecs
dc.subjectvícesíťová metodacs
dc.subjectkonvoluční neuronové sítěcs
dc.subjectautomatický kodércs
dc.titleFast Incremental Image Reconstruction with CNN-enhanced Poisson Interpolationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe present a novel image reconstruction method from scattered data based on multigrid relaxation of the Poisson equation and convolutional neural networks (CNN). We first formulate the image reconstruction problem as a Poisson equation with irregular boundary conditions, then propose a fast multigrid method for solving such an equation, and finally enhance the reconstructed image with a CNN to recover the details. The method works incrementally so that additional points can be added, and the amount of points does not affect the reconstruction speed. Furthermore, the multigrid and CNN techniques ensure that the output image resolution has only minor impact on the reconstruction speed. We evaluated the method on the CompCars dataset, where it achieves up to 40% error reduction compared to a reconstruction-only approach and 9% compared to a CNN-only approach.en
dc.subject.translatedimage recognitionen
dc.subject.translatednumerical interpolationen
dc.subject.translatedmultigrid methoden
dc.subject.translatedconvolutional neural networksen
dc.subject.translatedautoencoderen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.24
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2023: Full Papers Proceedings

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