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dc.contributor.authorEskelinen, Matti
dc.contributor.authorHämäläinen, Jyri
dc.date.accessioned2019-07-29T05:31:14Z
dc.date.available2019-07-29T05:31:14Z
dc.date.issued2019
dc.identifier.citationEskelinen, M., & Hämäläinen, J. (2019). Dangers of Demosaicing : Confusion From Correlation. In <i>WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing</i>. IEEE. <a href="https://doi.org/10.1109/WHISPERS.2018.8747204" target="_blank">https://doi.org/10.1109/WHISPERS.2018.8747204</a>
dc.identifier.otherCONVID_32243924
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65124
dc.description.abstractImages from colour sensors using Bayer filter arrays require demosaicing before viewing or further analysis. Advanced demosaicing methods use empirical knowledge of inter-channel correlations to reduce interpolation artefacts in the resulting images. These inter-channel correlations are however different for standard RGB cameras and hyperspectral imagers using colour sensors with added narrow-band spectral filtering. We study the effects of conventional demosaicing methods on hyperspectral images with a dataset originally collected without a colour filter array. We find that using advanced methods instead of bilinear interpolation results in an overall increase of 9–14 % in absolute error and a decrease of 1–3 % in PSNR, but also observed a decrease in MSE of 11–13 %. For the corresponding RGB images, the advanced methods improved fidelity as expected. The results also demonstrate that the reconstruction methods that take advantage of correlation transport noise present in a single component to other reconstructed layers.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofWHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.rightsIn Copyright
dc.subject.otherhyperspektrikuvantaminen
dc.subject.othercolour sensors
dc.subject.otherhyperspectral imagers
dc.titleDangers of Demosaicing : Confusion From Correlation
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-201907253684
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-07-25T12:15:08Z
dc.relation.isbn978-1-7281-1581-8
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatusnonPeerReviewed
dc.relation.issn2158-6276
dc.type.versionacceptedVersion
dc.rights.copyright© IEEE, 2019.
dc.rights.accesslevelopenAccessfi
dc.type.publicationconferenceObject
dc.relation.conferenceWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.subject.ysospektrikuvaus
dc.subject.ysokuvantaminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p3532
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/WHISPERS.2018.8747204
dc.type.okmB3


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