Files in this item
Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography
Item metadata
dc.contributor.author | Kazantsev, Daniil | |
dc.contributor.author | Beveridge, Lucas | |
dc.contributor.author | Shanmugasundar, Vigneshwar | |
dc.contributor.author | Magdysyuk, Oxana | |
dc.date.accessioned | 2024-01-22T12:30:09Z | |
dc.date.available | 2024-01-22T12:30:09Z | |
dc.date.issued | 2024-03 | |
dc.identifier | 296934751 | |
dc.identifier | b2e2a07f-93d6-4262-ba29-f50343ae63b7 | |
dc.identifier.citation | Kazantsev , 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 . https://doi.org/10.1016/j.tmater.2023.100019 | en |
dc.identifier.issn | 2949-673X | |
dc.identifier.other | RIS: urn:A58346F2FAF6ADBFFAA08F542DA5F910 | |
dc.identifier.other | ORCID: /0000-0003-3842-3239/work/147967154 | |
dc.identifier.uri | https://hdl.handle.net/10023/29040 | |
dc.description.abstract | Tomographic 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.format.extent | 13 | |
dc.format.extent | 9671788 | |
dc.language.iso | eng | |
dc.relation.ispartof | Tomography of Materials and Structures | en |
dc.subject | Image reconstruction | en |
dc.subject | Ring removal | en |
dc.subject | Sinogram inpainting | en |
dc.subject | Data extrapolation | en |
dc.subject | Deep learning | en |
dc.subject | GAN | en |
dc.subject | Material science | en |
dc.subject | Synchrotron | en |
dc.subject | X-ray tomography | en |
dc.subject | QD Chemistry | en |
dc.subject | DAS | en |
dc.subject.lcc | QD | en |
dc.title | Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. School of Chemistry | en |
dc.identifier.doi | 10.1016/j.tmater.2023.100019 | |
dc.description.status | Peer reviewed | en |
This item appears in the following Collection(s)
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.