Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning
The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting...
- 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:20.500.12585/9155
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9155
- Palabra clave:
- 3-D Reconstruction
Dictionary Learning
Image restoration
Phase unwrapping
Sparse representation
Gaussian noise (electronic)
Optical data processing
Restoration
Vision
White noise
3D reconstruction
Additive White Gaussian noise
Dictionary learning
Fringe projection profilometry
Image processing applications
Phase unwrapping
Sparse representation
Wrapped phase map
Image reconstruction
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| title |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| spellingShingle |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning 3-D Reconstruction Dictionary Learning Image restoration Phase unwrapping Sparse representation Gaussian noise (electronic) Optical data processing Restoration Vision White noise 3D reconstruction Additive White Gaussian noise Dictionary learning Fringe projection profilometry Image processing applications Phase unwrapping Sparse representation Wrapped phase map Image reconstruction |
| title_short |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| title_full |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| title_fullStr |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| title_full_unstemmed |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| title_sort |
Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning |
| dc.subject.keywords.none.fl_str_mv |
3-D Reconstruction Dictionary Learning Image restoration Phase unwrapping Sparse representation Gaussian noise (electronic) Optical data processing Restoration Vision White noise 3D reconstruction Additive White Gaussian noise Dictionary learning Fringe projection profilometry Image processing applications Phase unwrapping Sparse representation Wrapped phase map Image reconstruction |
| topic |
3-D Reconstruction Dictionary Learning Image restoration Phase unwrapping Sparse representation Gaussian noise (electronic) Optical data processing Restoration Vision White noise 3D reconstruction Additive White Gaussian noise Dictionary learning Fringe projection profilometry Image processing applications Phase unwrapping Sparse representation Wrapped phase map Image reconstruction |
| description |
The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting with dictionary learning to recover the continuous phase map. The method is validated both using numerically simulated data with strong additive white Gaussian noise and phase dislocations; and experimental data from fringe projection profilometry. Comparisons with other phase inpainting method referred to as PULSI+INTERP, show the suitability of the proposed method for phase restoration even in extremely noisy phases. The error given by the proposed method on the highest level of noise (RMSE=0.0269 Rad) remains the smallest compared to the error given by PULSI+INTERP for noise-free data (RMSE=0.0332 Rad). © 2019 IEEE. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:33:05Z |
| dc.date.available.none.fl_str_mv |
2020-03-26T16:33:05Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_c94f |
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info:eu-repo/semantics/conferenceObject |
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info:eu-repo/semantics/publishedVersion |
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Conferencia |
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publishedVersion |
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2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings |
| dc.identifier.isbn.none.fl_str_mv |
9781728114910 |
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https://hdl.handle.net/20.500.12585/9155 |
| dc.identifier.doi.none.fl_str_mv |
10.1109/STSIVA.2019.8730228 |
| dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57192270016 57204065355 57209542195 36142156300 24329839300 |
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2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings 9781728114910 10.1109/STSIVA.2019.8730228 Universidad Tecnológica de Bolívar Repositorio UTB 57192270016 57204065355 57209542195 36142156300 24329839300 |
| url |
https://hdl.handle.net/20.500.12585/9155 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.conferencedate.none.fl_str_mv |
24 April 2019 through 26 April 2019 |
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http://purl.org/coar/access_right/c_16ec |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/restrictedAccess |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
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restrictedAccess |
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Recurso electrónico |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
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Institute of Electrical and Electronics Engineers Inc. |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068049480&doi=10.1109%2fSTSIVA.2019.8730228&partnerID=40&md5=ace3337238ede20ac95bd15a31d715d3 Scopus2-s2.0-85068049480 |
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Universidad Tecnológica de Bolívar |
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22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 |
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2020-03-26T16:33:05Z2020-03-26T16:33:05Z20192019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings9781728114910https://hdl.handle.net/20.500.12585/915510.1109/STSIVA.2019.8730228Universidad Tecnológica de BolívarRepositorio UTB5719227001657204065355572095421953614215630024329839300The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting with dictionary learning to recover the continuous phase map. The method is validated both using numerically simulated data with strong additive white Gaussian noise and phase dislocations; and experimental data from fringe projection profilometry. Comparisons with other phase inpainting method referred to as PULSI+INTERP, show the suitability of the proposed method for phase restoration even in extremely noisy phases. The error given by the proposed method on the highest level of noise (RMSE=0.0269 Rad) remains the smallest compared to the error given by PULSI+INTERP for noise-free data (RMSE=0.0332 Rad). © 2019 IEEE.Universidad Tecnológica de Pereira, UTP: C2018P018, C2018P005 Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS: 538871552485IEEE Colombia Section;IEEE Signal Processing Society Colombia Chapter;Universidad Industrial de SantanderThis work has been partly funded by Colciencias project 538871552485, and by Universidad Tecnológica de Bolívar projects C2018P005 and C2018P018. J. Pineda and J. Meza thank Universidad Tecnológica de Bolívar for a Masters degree scholarship.Recurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://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-s2.0-85068049480&doi=10.1109%2fSTSIVA.2019.8730228&partnerID=40&md5=ace3337238ede20ac95bd15a31d715d3Scopus2-s2.0-8506804948022nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learninginfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94f3-D ReconstructionDictionary LearningImage restorationPhase unwrappingSparse representationGaussian noise (electronic)Optical data processingRestorationVisionWhite noise3D reconstructionAdditive White Gaussian noiseDictionary learningFringe projection profilometryImage processing applicationsPhase unwrappingSparse representationWrapped phase mapImage reconstruction24 April 2019 through 26 April 2019Pineda J.Meza J.Barrios E.M.Romero L.A.Marrugo A.G.Gorthi, S.S., Rastogi, P., Fringe projection techniques: Whither we are? (2010) Optics and Lasers in Engineering, 48, pp. 133-140. , IMACREVIEW-2009-001Bone, D.J., Fourier fringe analysis: The two-dimensional phase unwrapping problem (1991) Applied Optics, 30 (25), pp. 3627-3632Gdeisat, M.A., Burton, D.R., Lilley, F., Arevalillo-Herráez, M., Ammous, M.M.M., Aiding phase unwrapping by increasing the number of residues in two-dimensional wrapped-phase distributions (2015) Applied Optics, 54, pp. 10073-10078. , DecPineda, J., Vargas, R., Romero, L.A., Meneses, J., Marrugo, A.G., Fringe quality map for fringe projection profilometry in LabVIEW (2018) Opt. Pura Apl., 51, pp. 503021-503028. , DecZhao, M., Huang, L., Zhang, Q., Su, X., Asundi, A., Kemao, Q., Quality-guided phase unwrapping technique: Comparison of quality maps and guiding strategies (2011) Applied Optics, 50 (33), pp. 6214-6224Xu, J., An, D., Huang, X., Yi, P., An efficient minimumdiscontinuity phase-unwrapping method (2016) IEEE Geoscience and Remote Sensing Letters, 13 (5), pp. 666-670Yu, H., Lan, Y., Xu, J., An, D., Lee, H., Large-scale L0-norm and L1-norm 2-d phase unwrapping (2017) IEEE Transactions on Geoscience and Remote Sensing, 55, pp. 4712-4728. , AugXia, H., Montresor, S., Guo, R., Li, J., Olchewsky, F., Desse, J.-M., Picart, P., Robust processing of phase dislocations based on combined unwrapping and inpainting approaches (2017) Optics Letters, 42 (2), pp. 322-325Xia, H., Montresor, S., Picart, P., Guo, R., Li, J., Comparative analysis for combination of unwrapping and de-noising of phase data with high speckle decorrelation noise (2018) Optics and Lasers in Engineering, 107, pp. 71-77Meng, L., Fang, S., Yang, P., Wang, L., Komori, M., Kubo, A., Image-inpainting and quality-guided phase unwrapping algorithm (2012) Applied Optics, 51 (13), pp. 2457-2462Xia, H.-T., Guo, R.-X., Fan, Z.-B., Cheng, H.-M., Yang, B.-C., Non-invasive mechanical measurement for transparent objects by digital holographic interferometry based on iterative leastsquares phase unwrapping (2012) Experimental Mechanics, 52 (4), pp. 439-445Xia, H., Montresor, S., Guo, R., Li, J., Yan, F., Cheng, H., Picart, P., Phase calibration unwrapping algorithm for phase data corrupted by strong decorrelation speckle noise (2016) Optics Express, 24 (25), pp. 28713-28730Huang, K., Aviyente, S., Sparse representation for signal classification (2007) Advances in Neural Information Processing Systems, pp. 609-616Bruckstein, A.M., Donoho, D.L., Elad, M., From sparse solutions of systems of equations to sparse modeling of signals and images (2009) SIAM Review, 51 (1), pp. 34-81Manat, S., Zhang, Z., Matching pursuit in a time-frequency dictionary (1993) IEEE Trans Signal Processing, 12, pp. 3397-3451Tibshirani, R., Regression shrinkage and selection via the lasso (1996) Journal of the Royal Statistical Society. Series B (Methodological, pp. 267-288Rubinstein, R., Peleg, T., Elad, M., Analysis k-SVD: A dictionary-learning algorithm for the analysis sparse model (2013) IEEE Transactions on Signal Processing, 61 (3), pp. 661-677Elad, M., Starck, J.-L., Querre, P., Donoho, D.L., Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) (2005) Applied and Computational Harmonic Analysis, 19 (3), pp. 340-358Yang, J., Wright, J., Huang, T.S., Ma, Y., Image super-resolution via sparse representation (2010) IEEE Transactions on Image Processing, 19 (11), pp. 2861-2873Zhang, J., Zhao, D., Gao, W., Group-based sparse representation for image restoration (2014) IEEE Transactions on Image Processing, 23 (8), pp. 3336-3351Kwok, T.-H., Wang, C.C., Interactive image inpainting using dct based exemplar matching (2009) International Symposium on Visual Computing, pp. 709-718. , SpringerElad, M., Aharon, M., Image denoising via learned dictionaries and sparse representation (2006) 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 1, pp. 895-900. , IEEEGuleryuz, O.G., Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part i: Theory (2006) IEEE Transactions on Image Processing, 15 (3), pp. 539-554http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9155/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9155oai:repositorio.utb.edu.co:20.500.12585/91552021-02-02 14:44:04.584Repositorio Institucional UTBrepositorioutb@utb.edu.co |