Non-parametric sub-pixel local point spread function estimation

Almansa, Andrés - Musé, Pablo - Delbracio, Mauricio

Resumen:

This work presents an algorithm for the local subpixel estimation of the Point Spread Function (PSF) that models the intrinsic camera blur. For this purpose, the Bernoulli(0:5) random noise calibration pattern introduced in a previous article [1] is used. This leads to a well-posed near-optimal accurate estimation. First the pattern position and its illumination conditions are accurately estimated. This allows for accurate geometric registration and radiometric correction. Once these procedures are performed, the local PSF can be directly computed by inverting a linear system. This system is well-posed and consequently its inversion does not require any regularization or prior model. The PSF estimates reach stringent accuracy levels with a relative error in the order of 2 to 5%.


Detalles Bibliográficos
2012
Procesamiento de Señales
Inglés
Universidad de la República
COLIBRI
https://hdl.handle.net/20.500.12008/41147
https://doi.org/10.5201/ipol.2012.admm-nppsf
Acceso abierto
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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author Almansa, Andrés
author2 Musé, Pablo
Delbracio, Mauricio
author2_role author
author
author_facet Almansa, Andrés
Musé, Pablo
Delbracio, Mauricio
author_role author
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collection COLIBRI
dc.creator.none.fl_str_mv Almansa, Andrés
Musé, Pablo
Delbracio, Mauricio
dc.date.accessioned.none.fl_str_mv 2023-11-14T17:04:31Z
dc.date.available.none.fl_str_mv 2023-11-14T17:04:31Z
dc.date.issued.es.fl_str_mv 2012
dc.date.submitted.es.fl_str_mv 20231114
dc.description.abstract.none.fl_txt_mv This work presents an algorithm for the local subpixel estimation of the Point Spread Function (PSF) that models the intrinsic camera blur. For this purpose, the Bernoulli(0:5) random noise calibration pattern introduced in a previous article [1] is used. This leads to a well-posed near-optimal accurate estimation. First the pattern position and its illumination conditions are accurately estimated. This allows for accurate geometric registration and radiometric correction. Once these procedures are performed, the local PSF can be directly computed by inverting a linear system. This system is well-posed and consequently its inversion does not require any regularization or prior model. The PSF estimates reach stringent accuracy levels with a relative error in the order of 2 to 5%.
dc.identifier.citation.es.fl_str_mv Delbracio, M, Musé, P, Almansa, A. "Non-parametric sub-pixel local point spread function estimation", Image Processing On Line, 2 (2012), pp. 8–21. https://doi.org/10.5201/ipol.2012.admm-nppsf
dc.identifier.doi.es.fl_str_mv https://doi.org/10.5201/ipol.2012.admm-nppsf
dc.identifier.issn.es.fl_str_mv 2105-1232
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/41147
dc.language.iso.none.fl_str_mv en
eng
dc.publisher.es.fl_str_mv IPOL
dc.relation.ispartof.es.fl_str_mv Image Processing On Line, 2 (2012)
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.other.es.fl_str_mv Procesamiento de Señales
dc.title.none.fl_str_mv Non-parametric sub-pixel local point spread function estimation
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description This work presents an algorithm for the local subpixel estimation of the Point Spread Function (PSF) that models the intrinsic camera blur. For this purpose, the Bernoulli(0:5) random noise calibration pattern introduced in a previous article [1] is used. This leads to a well-posed near-optimal accurate estimation. First the pattern position and its illumination conditions are accurately estimated. This allows for accurate geometric registration and radiometric correction. Once these procedures are performed, the local PSF can be directly computed by inverting a linear system. This system is well-posed and consequently its inversion does not require any regularization or prior model. The PSF estimates reach stringent accuracy levels with a relative error in the order of 2 to 5%.
eu_rights_str_mv openAccess
format article
id COLIBRI_c2caf6edef282c96282847a89b9dd62a
identifier_str_mv Delbracio, M, Musé, P, Almansa, A. "Non-parametric sub-pixel local point spread function estimation", Image Processing On Line, 2 (2012), pp. 8–21. https://doi.org/10.5201/ipol.2012.admm-nppsf
2105-1232
instacron_str Universidad de la República
institution Universidad de la República
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language eng
language_invalid_str_mv en
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publishDate 2012
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
spelling 2023-11-14T17:04:31Z2023-11-14T17:04:31Z201220231114Delbracio, M, Musé, P, Almansa, A. "Non-parametric sub-pixel local point spread function estimation", Image Processing On Line, 2 (2012), pp. 8–21. https://doi.org/10.5201/ipol.2012.admm-nppsf2105-1232https://hdl.handle.net/20.500.12008/41147https://doi.org/10.5201/ipol.2012.admm-nppsfThis work presents an algorithm for the local subpixel estimation of the Point Spread Function (PSF) that models the intrinsic camera blur. For this purpose, the Bernoulli(0:5) random noise calibration pattern introduced in a previous article [1] is used. This leads to a well-posed near-optimal accurate estimation. First the pattern position and its illumination conditions are accurately estimated. This allows for accurate geometric registration and radiometric correction. Once these procedures are performed, the local PSF can be directly computed by inverting a linear system. This system is well-posed and consequently its inversion does not require any regularization or prior model. The PSF estimates reach stringent accuracy levels with a relative error in the order of 2 to 5%.Made available in DSpace on 2023-11-14T17:04:31Z (GMT). No. of bitstreams: 5 DMA12.pdf: 1616442 bytes, checksum: cdbdc36773461afea4220085aca57a1a (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2012enengIPOLImage Processing On Line, 2 (2012)Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Procesamiento de SeñalesNon-parametric sub-pixel local point spread function estimationArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaAlmansa, AndrésMusé, PabloDelbracio, MauricioProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse
spellingShingle Non-parametric sub-pixel local point spread function estimation
Almansa, Andrés
Procesamiento de Señales
status_str publishedVersion
title Non-parametric sub-pixel local point spread function estimation
title_full Non-parametric sub-pixel local point spread function estimation
title_fullStr Non-parametric sub-pixel local point spread function estimation
title_full_unstemmed Non-parametric sub-pixel local point spread function estimation
title_short Non-parametric sub-pixel local point spread function estimation
title_sort Non-parametric sub-pixel local point spread function estimation
topic Procesamiento de Señales
url https://hdl.handle.net/20.500.12008/41147
https://doi.org/10.5201/ipol.2012.admm-nppsf

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