dc.contributorAltuve M.
dc.creatorMarrugo A.G.
dc.creatorVargas R.
dc.creatorContreras Ortiz, Sonia Helena
dc.creatorMillan M.S.
dc.date.accessioned2020-03-26T16:32:42Z
dc.date.available2020-03-26T16:32:42Z
dc.date.created2020-03-26T16:32:42Z
dc.date.issued2016
dc.identifier2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
dc.identifier9781509037971
dc.identifierhttps://hdl.handle.net/20.500.12585/8978
dc.identifier10.1109/STSIVA.2016.7743327
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier24329839300
dc.identifier57117284600
dc.identifier57210822856
dc.identifier7201466399
dc.description.abstractRetinal eye fundus images are used for diagnostic purposes, but despite controlled conditions in acquisition they often suffer from uneven illumination and blur. In this work, we propose the use of multi-channel blind deconvolution for the restoration of blurred retinal images. The estimation of an adequate point-spread function (PSF) is highly dependent on the registration of at least two images from the same retina, which undergo illumination compensation. We use the bi-dimensional empirical mode decomposition (BEMD) approach to model the illumination distribution as a sum of non-stationary signals. The BEMD approach enables an artifact-free compensation of the illumination in order to estimate an adequate PSF and carry out the best restoration possible. Encouraging experimental results show significant enhancement in the retinal images with increased contrast and visibility of subtle details like small blood vessels. © 2016 IEEE.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation30 August 2016 through 2 September 2016
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85002970865&doi=10.1109%2fSTSIVA.2016.7743327&partnerID=40&md5=f0903522fcb6848666232c0a900c8fc1
dc.sourceScopus2-s2.0-85002970865
dc.source21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
dc.titleOn the compensation of uneven illumination in retinal images for restoration by means of blind deconvolution


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