Experiments in Evolutionary Image Enhancement with ELAINE

UDC.coleccionInvestigación
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.endPage579
UDC.grupoInvRNASA - IMEDIR (INIBIC)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.journalTitleGenetic Programming and Evolvable Machines
UDC.startPage557
UDC.volume23
dc.contributor.authorCorreia, Joao
dc.contributor.authorLopes, Daniel
dc.contributor.authorVieira, Leonardo
dc.contributor.authorRodríguez-Fernández, Nereida
dc.contributor.authorCarballal, Adrián
dc.contributor.authorRomero, Juan
dc.contributor.authorMachado, Penousal
dc.date.accessioned2026-02-06T18:56:11Z
dc.date.available2026-02-06T18:56:11Z
dc.date.issued2022
dc.description“This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10710-022-09445-9. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms’
dc.description.abstract[Abstract]: Image enhancement is an image processing procedure in which the image’s original information is refined, for example by highlighting specific features to ease post-processing analyses by a human or machine. This procedure remains challenging since each set of images is often taken under diverse conditions which makes it hard to find an image enhancement solution that fits all conditions. State-of-the-art image enhancement pipelines apply filters that solve specific issues; therefore, it is still hard to generalise these pipelines to all types of problems encountered. We have recently introduced a Genetic Programming approach named ELAINE (EvoLutionAry Image eNhancEment) for evolving image enhancement pipelines based on pre-defined image filters. In this paper, we showcase its potential to create solutions under a real-estate marketing scenario by comparing it with a manual approach and an existing tool for automatic image enhancement. The ELAINE obtained results far exceed those obtained by manual combinations of filters and by the one-click method, in all the metrics explored. We further explore the potential of creating non-photorealistic effects by applying the evolved pipelines to different types of images. The results highlight ELAINE’s potential to transform input images into either suitable real-estate images or nonphotorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.
dc.description.sponsorshipThis work is funded by the Foundation for Science and Technology (FCT), I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit - UIDB/00326/2020 or project code UIDP/00326/2020 and under the grant SFRH/BD/143553/2019. This work is also funded by the INDITEX-UDC Program for predoctoral research stays through the Collaboration Agreement between the UDC and INDITEX for the internationalization of doctoral studies. Juan Romero received funding from Spanish Ministry of Universities for mobility stays of professors and researchers in foreign centres of higher education and research. Juan Romero and Adrian Carballal received funding with reference PID2020-118362RB-I00, from the State Program of R+D+i Oriented to the Challenges of the Society of the Spanish Ministry of Science, Innovation and Universities.
dc.description.sponsorshipPortugal. Fundação para a Ciência e a Tecnologia; UIDP/00326/2020
dc.description.sponsorshipPortugal. Fundação para a Ciência e a Tecnologia; SFRH/BD/143553/2019
dc.identifier.citationCorreia, J., Lopes, D., Vieira, L. et al. Experiments in evolutionary image enhancement with ELAINE. Genet Program Evolvable Mach 23, 557–579 (2022). https://doi.org/10.1007/s10710-022-09445-9
dc.identifier.doi10.1007/s10710-022-09445-9
dc.identifier.issn1389-2576
dc.identifier.issn1573-7632
dc.identifier.urihttps://hdl.handle.net/2183/47288
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118362RB-I00/ES/PREDICCION DE LA PERCEPCION ESTETICA HUMANA MEDIANTE INTELIGENCIA ARTIFICIAL/
dc.relation.urihttps://doi.org/10.1007/s10710-022-09445-9
dc.rights.accessRightsopen access
dc.subjectImage Enhancement
dc.subjectImage Processing
dc.subjectComputer Vision
dc.subjectEvolutionary Computation
dc.subjectGenetic Programming
dc.titleExperiments in Evolutionary Image Enhancement with ELAINE
dc.typejournal article
dc.type.hasVersionAM
dspace.entity.typePublication
relation.isAuthorOfPublicationd55eb504-ada8-45a7-b9c1-7f1d537f3641
relation.isAuthorOfPublication6f70022e-b21b-4255-9693-e1402a9e4750
relation.isAuthorOfPublicationf9985d38-4af2-414c-8e8c-0e46c78c60e7
relation.isAuthorOfPublication.latestForDiscoveryd55eb504-ada8-45a7-b9c1-7f1d537f3641

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