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FocusNET : an autofocusing learning‐based model for digital lensless holographic microscopy

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dc.contributor.advisorTrujillo Anaya, Carlos Alejandro
dc.contributor.advisorLopera Acosta, María Josef
dc.contributor.authorMontoya Zuluaga, Manuel
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.emailmmonto95@eafit.edu.cospa
dc.date.accessioned2023-08-15T16:42:43Z
dc.date.available2023-08-15T16:42:43Z
dc.date.issued2023
dc.description.abstractThis paper reports on a convolutional neural network (CNN) – based regression model, called FocusNET, to predict the accurate reconstruction distance of raw holograms in Digital Lensless Holographic Microscopy (DLHM). This proposal provides a physical-mathematical formulation to extend its use to different DLHM setups than the optical and geometrical conditions utilized for recording the training dataset; this unique feature is tested by applying the proposal to holograms of diverse samples recorded with different DLHM setups. Additionally, a comparison between FocusNET and conventional autofocusing methods in terms of processing times and accuracy is provided. Although the proposed method predicts reconstruction distances with approximately 54 µm standard deviation, accurate information about the samples in the validation dataset is still retrieved. When compared to a method that utilizes a stack of reconstructions to find the best focal plane, FocusNET performs 600 times faster, as no hologram reconstruction is needed. When implemented in batches, the network can achieve up to a 1200-fold reduction in processing time, depending on the number of holograms to be processed. The training and validation datasets, and the code implementations, are hosted on a public GitHub repository that can be freely accessed.spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias de Datos y Analíticaspa
dc.format.mimetypeapplication/pdf
dc.identifier.ddc621.36 M798
dc.identifier.instnameinstname:Universidad EAFIT
dc.identifier.reponamereponame:Repositorio Institucional Universidad EAFIT
dc.identifier.repourlrepourl:https://repository.eafit.edu.co
dc.identifier.urihttps://hdl.handle.net/10784/32788
dc.language.isoeng
dc.publisherUniversidad EAFITspa
dc.publisher.departmentÁrea Computación y Analíticaspa
dc.publisher.facultyEscuela de Ciencias Aplicadas e Ingenieríaspa
dc.publisher.placeMedellínspa
dc.publisher.programMaestría en Ciencias de los Datos y Analíticaspa
dc.relation.urihttps://doi.org/10.1016/j.optlaseng.2023.107546spa
dc.rightsTodos los derechos reservadosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.localAcceso abierto
dc.rights.localspa
dc.subjectMicroscopía sin lentesspa
dc.subjectAutoenfoquespa
dc.subjectAprendizaje profundospa
dc.subjectRed neuronal convolucionalspa
dc.subjectHolografía digital de Gaborspa
dc.subject.keywordLensfree microscopyspa
dc.subject.keywordAutofocusingspa
dc.subject.keywordDeep Learningspa
dc.subject.keywordConvolutional neural networkspa
dc.subject.keywordDigital Gabor Holographyspa
dc.subject.lembAPRENDIZAJE AUTOMÁTICO (INTELIGENCIA ARTIFICIAL)spa
dc.subject.lembHOLOGRAFÍAspa
dc.subject.lembMICROSCOPÍAspa
dc.subject.lembÓPTICAspa
dc.titleFocusNET : an autofocusing learning‐based model for digital lensless holographic microscopy
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.localTesis de Maestríaspa
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication

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