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Dataset for Training Generative Adversarial Networks for Optical Property Mapping using Synthetic Image Data

dc.contributor.authorGordon, George
dc.contributor.otherOsman, Ahmed
dc.contributor.otherCrowley, Jane
dc.date.accessioned2022-06-23T07:45:03Z
dc.date.available2022-06-23T07:45:03Z
dc.date.issued2022-06-30
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/9537
dc.description.abstractData and code used to generate deep learning model to generate results in the above paperen_UK
dc.language.isoenen_UK
dc.publisherThe University of Nottinghamen_UK
dc.rightsCC-BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.lcshNeural networks (Computer science)en_UK
dc.subject.lcshImage processing -- Digital techniquesen_UK
dc.subject.lcshOptical data processingen_UK
dc.titleDataset for Training Generative Adversarial Networks for Optical Property Mapping using Synthetic Image Dataen_UK
dc.typedataset
dc.identifier.doihttp://doi.org/10.17639/nott.7219
dc.subject.freeBlender, spatial frequency domain imaging, generative adversarial network, domain transferen_UK
dc.subject.jacsEngineering::Electronic & electrical engineering::Optoelectronic engineeringen_UK
dc.subject.lcT Technology::TK Electrical engineering. Electronics Nuclear engineeringen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Engineering::Department of Electrical and Electronic Engineeringen_UK
uon.funder.controlledOtheren_UK
uon.datatypeImages generated by Blender softwareen_UK
uon.funder.freeUK Research and Innovationen_UK
uon.grantMR/T041951/1en_UK
uon.grant2268555en_UK
uon.collectionmethodSimulated data using Blenderen_UK
uon.institutes-centresUniversity of Nottingham, UK Campusen_UK
uon.preservation.rarelyaccessedtrue
uon.identifier.risproject2653401en_UK


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