Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy

White light scanning interference (WLSI) microscopes provide an accurate surface topography of engineered surfaces. However, the measurement accuracy is substantially reduced in surfaces with low-reflectivity regions or high roughness, like a surface affected by corrosion. An alternative technique c...

Full description

Autores:
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/9164
Acceso en línea:
https://hdl.handle.net/20.500.12585/9164
Palabra clave:
Corrosion
Recovery
Reflection
Textures
3D reconstruction
Accurate measurement
Engineered surfaces
Extensive simulations
Interference microscopy
Measurement accuracy
Standard deviation
Surface reflectivity
Surface roughness
Rights
restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_d7fb6c056220e363b4e6529580fc7dd9
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/9164
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.none.fl_str_mv Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
title Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
spellingShingle Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
Corrosion
Recovery
Reflection
Textures
3D reconstruction
Accurate measurement
Engineered surfaces
Extensive simulations
Interference microscopy
Measurement accuracy
Standard deviation
Surface reflectivity
Surface roughness
title_short Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
title_full Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
title_fullStr Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
title_full_unstemmed Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
title_sort Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopy
dc.subject.keywords.none.fl_str_mv Corrosion
Recovery
Reflection
Textures
3D reconstruction
Accurate measurement
Engineered surfaces
Extensive simulations
Interference microscopy
Measurement accuracy
Standard deviation
Surface reflectivity
Surface roughness
topic Corrosion
Recovery
Reflection
Textures
3D reconstruction
Accurate measurement
Engineered surfaces
Extensive simulations
Interference microscopy
Measurement accuracy
Standard deviation
Surface reflectivity
Surface roughness
description White light scanning interference (WLSI) microscopes provide an accurate surface topography of engineered surfaces. However, the measurement accuracy is substantially reduced in surfaces with low-reflectivity regions or high roughness, like a surface affected by corrosion. An alternative technique called shape from focus (SFF) takes advantage of the surface texture to recover the 3D surface by using a focus metric through a vertical scan. In this work, we propose a technique called SFF-WLSI, which consists of recovering the 3D surface of an object by applying the Tenegrad Variance (TENV) focus metric to WLSI images. Extensive simulation results show that the proposed technique yields accurate measurements under different surface roughness and surface reflectivity, outperforming the conventional WLSI and the SFF techniques. We validated the simulation results on two real objects with a Mirau-type microscope. The first was a flat lapping specimen with R a 0.05 μm for which we measured an average value of R a 0.055 μm and standard deviation σ 0.008 μm. The second was a metallic sphere with corrosion, which we reconstructed with WLSI versus the proposed SFF-WLSI technique, producing a better 3D reconstruction with less undefined depth values. © 2018 Optical Society of America.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-03-26T16:33:06Z
dc.date.available.none.fl_str_mv 2020-03-26T16:33:06Z
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.hasversion.none.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.none.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Applied Optics; Vol. 58, Núm. 5; pp. A101-A111
dc.identifier.issn.none.fl_str_mv 1559128X
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/9164
dc.identifier.doi.none.fl_str_mv 10.1364/AO.58.00A101
dc.identifier.instname.none.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv Repositorio UTB
dc.identifier.orcid.none.fl_str_mv 57203321995
57190688459
24329839300
identifier_str_mv Applied Optics; Vol. 58, Núm. 5; pp. A101-A111
1559128X
10.1364/AO.58.00A101
Universidad Tecnológica de Bolívar
Repositorio UTB
57203321995
57190688459
24329839300
url https://hdl.handle.net/20.500.12585/9164
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
dc.rights.cc.none.fl_str_mv Atribución-NoComercial 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial 4.0 Internacional
http://purl.org/coar/access_right/c_16ec
eu_rights_str_mv restrictedAccess
dc.format.medium.none.fl_str_mv Recurso electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv OSA - The Optical Society
publisher.none.fl_str_mv OSA - The Optical Society
dc.source.none.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061456342&doi=10.1364%2fAO.58.00A101&partnerID=40&md5=b4086b44e69abf93da4c508632ad8145
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/9164/1/MiniProdInv.png
bitstream.checksum.fl_str_mv 0cb0f101a8d16897fb46fc914d3d7043
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1814021788268494848
spelling 2020-03-26T16:33:06Z2020-03-26T16:33:06Z2019Applied Optics; Vol. 58, Núm. 5; pp. A101-A1111559128Xhttps://hdl.handle.net/20.500.12585/916410.1364/AO.58.00A101Universidad Tecnológica de BolívarRepositorio UTB572033219955719068845924329839300White light scanning interference (WLSI) microscopes provide an accurate surface topography of engineered surfaces. However, the measurement accuracy is substantially reduced in surfaces with low-reflectivity regions or high roughness, like a surface affected by corrosion. An alternative technique called shape from focus (SFF) takes advantage of the surface texture to recover the 3D surface by using a focus metric through a vertical scan. In this work, we propose a technique called SFF-WLSI, which consists of recovering the 3D surface of an object by applying the Tenegrad Variance (TENV) focus metric to WLSI images. Extensive simulation results show that the proposed technique yields accurate measurements under different surface roughness and surface reflectivity, outperforming the conventional WLSI and the SFF techniques. We validated the simulation results on two real objects with a Mirau-type microscope. The first was a flat lapping specimen with R a 0.05 μm for which we measured an average value of R a 0.055 μm and standard deviation σ 0.008 μm. The second was a metallic sphere with corrosion, which we reconstructed with WLSI versus the proposed SFF-WLSI technique, producing a better 3D reconstruction with less undefined depth values. © 2018 Optical Society of America.Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS: 538871552485 Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS: 785-2017Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS) (538871552485, 785-2017). Parts of this work were presented at the Imaging and Applied Optics Congress 2018, Orlando, Florida, 2018, paper JTu4A.19.Recurso electrónicoapplication/pdfengOSA - The Optical Societyhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061456342&doi=10.1364%2fAO.58.00A101&partnerID=40&md5=b4086b44e69abf93da4c508632ad8145Robust 3D surface recovery by applying a focus criterion in white light scanning interference microscopyinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1CorrosionRecoveryReflectionTextures3D reconstructionAccurate measurementEngineered surfacesExtensive simulationsInterference microscopyMeasurement accuracyStandard deviationSurface reflectivitySurface roughnessAltamar Mercado, HernandoPatiño Vanegas, AlbertoMarrugo A.G.Harding, K., (2013) Handbook of Optical Dimensional Metrology, , CRC PressGianto, G., Salzenstein, F., Montgomery, P., Comparison of envelope detection techniques in coherence scanning interferometry (2016) Appl. Opt., 55, pp. 6763-6774Larkin, K.G., Efficient nonlinear algorithm for envelope detection in white light interferometry (1996) J. Opt. Soc. Am. A, 13, pp. 832-843Montgomery, P., Salzenstein, F., Montaner, D., Serio, B., Pfeiffer, P., Implementation of a fringe visibility based algorithm in coherence scanning interferometry for surface roughness measurement (2013) Proc. SPIE, 8788, p. 87883GDresel, T., Häusler, G., Venzke, H., Three-dimensional sensing of rough surfaces by coherence radar (1992) Appl. Opt., 31, pp. 919-925Gao, F., Leach, R.K., Petzing, J., Coupland, J.M., Surface measurement errors using commercial scanning white light interferometers (2008) Meas. Sci. Technol., 19, p. 015303Noguchi, M., Nayar, S.K., Microscopic shape from focus using a projected illumination pattern (1996) Math. Comput. Model., 24, pp. 31-48Florczak, J., Usage of shape from focus method for 3D shape recovery and identification of 3D object position (2014) Int. J. Image Process., 8, pp. 116-124Helmli, F., Scherer, S., Adaptive shape from focus with an error estimation in light microscopy (2001) 2nd International Symposium on Image and Signal Processing and Analysis, pp. 188-193Tian, Y., Hu, H., Cui, H., Yang, S., Qi, J., Xu, Z., Li, L., Three-dimensional surface microtopography recovery from a multifocus image sequence using an omnidirectional modified Laplacian operator with adaptive window size (2017) Appl. Opt., 56, pp. 6300-6310Pertuz, S., Puig, D., García, M.A., Analysis of focus measure operators for shape-from-focus (2013) Pattern Recogn, 46, pp. 1415-1432Xu, X., Wang, Y., Zhang, X., Li, S., Liu, X., Wang, X., Tang, J., A comparison of contrast measurements in passive autofocus systems for low contrast images (2014) Multimedia Tools Appl, 69, pp. 139-156Lee, I., Mahmood, M.T., Shim, S.O., Lee, S.A., Choi, T.S., Depth estimation based on blur measurement for three dimensional camera (2013) IEEE International Conference on Consumer Electronics, pp. 262-263Fan, T., Yu, H., A novel shape from focus method based on 3D steerable filters for improved performance on treating textureless region (2018) Opt. Commun., 410, pp. 254-261Liżewski, K., Tomczewski, S., Kozacki, T., Kostencka, J., High-precision topography measurement through accurate in-focus plane detection with hybrid digital holographic microscope and white light interferometer module (2014) Appl. Opt., 53, pp. 2446-2454Filipinas, J.L.D.C., Almoro, P.F., Vibration detection using focus analysis of interferograms (2012) Appl. Opt., 51, pp. 1431-1435Lim, Y.-T., Park, J.-H., Kwon, K.-C., Kim, N., Analysis on enhanced depth of field for integral imaging microscope (2012) Opt. Express, 20, pp. 23480-23488Sobel, G., Feldman, I., A 3 × 3 isotropic gradient operator for image processing (1968) Stanford Artificial Intelligence Project (SAIL)Vo, Q., Fang, F., Zhang, X., Gao, H., Surface recovery algorithm in white light interferometry based on combined white light phase shifting and fast Fourier transform algorithms (2017) Appl. Opt., 56, pp. 8174-8185Ausloos, M., Berman, D., A multivariate weierstrass-mandelbrot function (1985) Proc. R. Soc. Lond. A, 400, pp. 331-350Yan, W., Komvopoulos, K., Contact analysis of elastic-plastic fractal surfaces (1998) J. Appl. Phys., 84, pp. 3617-3624Ragheb, H., Hancock, E.R., Rough surface analysis using Kirchhoff theory (2003) British Machine Vision Conference, pp. 20.1–20.10. , British Machine Vision AssociationChoi, N., Harvey, J.E., Image degradation due to surface scatter in the presence of aberrations (2012) Appl. Opt., 51, pp. 535-546Malacara, D., (2007) Optical Shop Testing, 59. , WileyKino, G.S., Corle, T.R., (1996) Confocal Scanning Optical Microscopy and Related Imaging Systems, , AcademicSubbarao, M., Focusing techniques (1993) Opt. Eng., 32, pp. 2824-2836Pentland, A.P., A new sense for depth of field (1987) IEEE Trans. Pattern Anal. Mach. Intel., PAMI-9, pp. 523-531http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9164/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9164oai:repositorio.utb.edu.co:20.500.12585/91642023-05-25 10:16:29.098Repositorio Institucional UTBrepositorioutb@utb.edu.co