A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images

Autores
Ferraggine, Viviana; Villar, Sebastián
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión aceptada
Descripción
Sonar images are typically affected by a granular pattern interference known as speckle noise, which degrades image contrast. To aid in object detection and recognition for speckled imagery, a robust version of the Lee filter is presented. The new method essentially combines robust statistics with an adaptive approach to achieve an effective balance between contrast stretching and speckle reduction. Tests were performed on real sonar images, where objective metrics and direct visual perception were used to evaluate the results. Experiments have shown that this easy-to-implement filter remarkably highlights edges and details with apparent speckle reduction, offering a promising simple tool that may be useful in segmentation and classification applications.
Publicado en: 2020 IEEE Congreso Bienal de Argentina (ARGENCON)
Materia
Ciencias de la Computación e Información
Side Scan Sonar
Noise reduction
Image enhancement
Robust statistics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/11080
Acceder
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network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar imagesFerraggine, VivianaVillar, SebastiánCiencias de la Computación e InformaciónSide Scan SonarNoise reductionImage enhancementRobust statisticsSonar images are typically affected by a granular pattern interference known as speckle noise, which degrades image contrast. To aid in object detection and recognition for speckled imagery, a robust version of the Lee filter is presented. The new method essentially combines robust statistics with an adaptive approach to achieve an effective balance between contrast stretching and speckle reduction. Tests were performed on real sonar images, where objective metrics and direct visual perception were used to evaluate the results. Experiments have shown that this easy-to-implement filter remarkably highlights edges and details with apparent speckle reduction, offering a promising simple tool that may be useful in segmentation and classification applications.Publicado en: 2020 IEEE Congreso Bienal de Argentina (ARGENCON)2020-12info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/11080enginfo:eu-repo/semantics/altIdentifier/doi/10.1109/ARGENCON49523.2020.9505346info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:11Zoai:digital.cic.gba.gob.ar:11746/11080Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:11.696CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
title A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
spellingShingle A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
Ferraggine, Viviana
Ciencias de la Computación e Información
Side Scan Sonar
Noise reduction
Image enhancement
Robust statistics
title_short A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
title_full A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
title_fullStr A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
title_full_unstemmed A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
title_sort A robust version of the Lee filter for speckle reduction and contrast enhancement applied to side scan sonar images
dc.creator.none.fl_str_mv Ferraggine, Viviana
Villar, Sebastián
author Ferraggine, Viviana
author_facet Ferraggine, Viviana
Villar, Sebastián
author_role author
author2 Villar, Sebastián
author2_role author
dc.subject.none.fl_str_mv Ciencias de la Computación e Información
Side Scan Sonar
Noise reduction
Image enhancement
Robust statistics
topic Ciencias de la Computación e Información
Side Scan Sonar
Noise reduction
Image enhancement
Robust statistics
dc.description.none.fl_txt_mv Sonar images are typically affected by a granular pattern interference known as speckle noise, which degrades image contrast. To aid in object detection and recognition for speckled imagery, a robust version of the Lee filter is presented. The new method essentially combines robust statistics with an adaptive approach to achieve an effective balance between contrast stretching and speckle reduction. Tests were performed on real sonar images, where objective metrics and direct visual perception were used to evaluate the results. Experiments have shown that this easy-to-implement filter remarkably highlights edges and details with apparent speckle reduction, offering a promising simple tool that may be useful in segmentation and classification applications.
Publicado en: 2020 IEEE Congreso Bienal de Argentina (ARGENCON)
description Sonar images are typically affected by a granular pattern interference known as speckle noise, which degrades image contrast. To aid in object detection and recognition for speckled imagery, a robust version of the Lee filter is presented. The new method essentially combines robust statistics with an adaptive approach to achieve an effective balance between contrast stretching and speckle reduction. Tests were performed on real sonar images, where objective metrics and direct visual perception were used to evaluate the results. Experiments have shown that this easy-to-implement filter remarkably highlights edges and details with apparent speckle reduction, offering a promising simple tool that may be useful in segmentation and classification applications.
publishDate 2020
dc.date.none.fl_str_mv 2020-12
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/11080
url https://digital.cic.gba.gob.ar/handle/11746/11080
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/ARGENCON49523.2020.9505346
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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