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A super resolution method based on generative adversarial networks with quantum feature enhancement: Application to aerial agricultural images

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dc.contributor.authorEl amraoui, Khalid
dc.contributor.authorPu, Ziqiang
dc.contributor.authorKoutti, Lahcen
dc.contributor.authorMasmoudi, Lhoussaine
dc.contributor.authorValente de Oliveira, JOSÉ
dc.date.accessioned2024-04-04T12:48:28Z
dc.date.available2024-04-04T12:48:28Z
dc.date.issued2024
dc.description.abstractSuper-resolution aims to enhance the quality of a low-resolution image to create a high-resolution one. Remarkable advances are witnessed in this field using machine learning techniques. This paper presents a superresolution method based on generative adversarial networks (GAN) with quantum feature enhancement. The proposed framework uses a feature enhancement layer inspired by the quantum superposition principle. The layer was added to the state -of -the art super-resolution GAN (SRGAN) original model to enhance its performance. The model was trained and evaluated using two publicly available high-resolution aerial images datasets taken by an unmanned aerial vehicle. A set of statistically significant experiments are reported to show its performance. The structural similarity index metric (SSIM), t-distributed stochastic neighbor embedding (t -SNE) and peak signal-to-noise ratio (PSNR) are adopted to evaluate the performance of this proposal against SRGAN model. Results show that this proposal outperforms SRGAN in term of image reconstruction quality by 8% in similarity.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.neucom.2024.127346pt_PT
dc.identifier.eissn1872-8286
dc.identifier.urihttp://hdl.handle.net/10400.1/20593
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.subjectSuper-resolutionpt_PT
dc.subjectDeep learningpt_PT
dc.subjectQuantum image processingpt_PT
dc.titleA super resolution method based on generative adversarial networks with quantum feature enhancement: Application to aerial agricultural imagespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.startPage127346pt_PT
oaire.citation.titleNeurocomputingpt_PT
oaire.citation.volume577pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0001-5337-5699
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b
relation.isProjectOfPublication9df77b70-8231-47e7-9b34-c702e9c6021c
relation.isProjectOfPublication.latestForDiscovery9df77b70-8231-47e7-9b34-c702e9c6021c

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