Multi-resolution X-ray tomography of a lattice structure for super-resolution using deep learning

DOI

Data originally supporting the article: Klos, A., Salvo, L. & Lhuissier, P. Super-resolution X-ray tomography using deep learning applied to the 3D quantification of defects in lattice structures. Sci Rep 15, 36664 (2025). https://doi.org/10.1038/s41598-025-20372-4 This includes registered X-ray tomography volumes used for performing the deep-learning super-resolution training, validation and test.

Identifier
DOI https://doi.org/10.57745/VDKKAH
Related Identifier IsCitedBy https://doi.org/10.1038/s41598-025-20372-4
Related Identifier IsCitedBy https://doi.org/10.1007/s10921-025-01273-y
Metadata Access https://entrepot.recherche.data.gouv.fr/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.57745/VDKKAH
Provenance
Creator Klos, Antoine ORCID logo; Salvo, Luc ORCID logo; Lhuissier, Pierre ORCID logo
Publisher Recherche Data Gouv
Contributor Klos, Antoine; Science et ingénierie des matériaux et des procédés; Entrepôt Recherche Data Gouv
Publication Year 2025
Funding Reference Agence nationale de la recherche ANR-10-LABX-0044
Rights etalab 2.0; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/etalab-2.0.html
OpenAccess true
Contact Klos, Antoine (SIMaP ; UGA, CNRS, Grenoble INP ; France)
Representation
Resource Type Dataset
Format text/comma-separated-values; text/plain; image/tiff
Size 1189; 3619; 3962860026; 248654180; 3909543460; 244534336
Version 1.1
Discipline Computer Science; Engineering Sciences; Construction Engineering and Architecture; Engineering