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dc.contributor.authorKazantsev, Daniil
dc.contributor.authorBeveridge, Lucas
dc.contributor.authorShanmugasundar, Vigneshwar
dc.contributor.authorMagdysyuk, Oxana
dc.identifier.citationKazantsev , D , Beveridge , L , Shanmugasundar , V & Magdysyuk , O 2024 , ' Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography ' , Tomography of Materials and Structures , vol. 4 , 100019 .
dc.identifier.otherRIS: urn:A58346F2FAF6ADBFFAA08F542DA5F910
dc.identifier.otherORCID: /0000-0003-3842-3239/work/147967154
dc.description.abstractTomographic imaging supports a great number of medical and material science applications. The collected projection data usually has different types of imaging artefacts and noise. Various image pre-processing and reconstruction methods are used to obtain volumetric datasets of high quality for further analysis. In order to minimise reconstruction artefacts, one can apply either filtering and/or data completion/inpainting techniques which can recover the data. Deep learning (DL) methods to remove artefacts and noise have been successfully applied in the past. In this paper, we present a novel approach based on conditional generative adversarial networks (cGANs) to remove stripe artefacts. The novelty of the presented technique is in how the training data for DL is extracted from the same tomographic dataset that needs recovery. We also provide new deterministic stripe detection and inpainting algorithms to support the development. The presented methods are compared with other stripe removal algorithms and applied to 3D and 4D high-resolution X-ray data collected at Diamond Light Source synchrotron, UK. The proposed DL method delivers reconstructed images with minimised ring artefacts while being a parameter-free approach. A similar DL strategy can also be applied to remove other types of artefacts in images.
dc.relation.ispartofTomography of Materials and Structuresen
dc.subjectImage reconstructionen
dc.subjectRing removalen
dc.subjectSinogram inpaintingen
dc.subjectData extrapolationen
dc.subjectDeep learningen
dc.subjectMaterial scienceen
dc.subjectX-ray tomographyen
dc.subjectQD Chemistryen
dc.titleConditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomographyen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.description.statusPeer revieweden

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