Restoration of retinal images with space-variant blur
Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of v...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2014
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9066
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9066
- Palabra clave:
- Blind deconvolution
Deblurring
Image restoration
Retinal image
Space-variant restoration
Blind deconvolution
Clinical resources
Deblurring
Innovative strategies
Point-spread functions
Restoration methods
Retinal image
Space variants
Aberrations
Convolution
Diagnosis
Image reconstruction
Ophthalmology
Restoration
Image enhancement
Algorithm
Angiography
Article
Artifact
Astigmatism
Automated pattern recognition
Eye fundus
Human
Image processing
Methodology
Normal distribution
Optics
Pathology
Reproducibility
Retina
Retina blood vessel
Statistical model
Vision
Visual system examination
Algorithms
Angiography
Artifacts
Astigmatism
Diagnostic Techniques, Ophthalmological
Fundus Oculi
Humans
Image Processing, Computer-Assisted
Models, Statistical
Normal distribution
Optics and Photonics
Pattern Recognition, Automated
Reproducibility of Results
Retina
Retinal Vessels
Vision, Ocular
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Restoration of retinal images with space-variant blur |
| title |
Restoration of retinal images with space-variant blur |
| spellingShingle |
Restoration of retinal images with space-variant blur Blind deconvolution Deblurring Image restoration Retinal image Space-variant restoration Blind deconvolution Clinical resources Deblurring Innovative strategies Point-spread functions Restoration methods Retinal image Space variants Aberrations Convolution Diagnosis Image reconstruction Ophthalmology Restoration Image enhancement Algorithm Angiography Article Artifact Astigmatism Automated pattern recognition Eye fundus Human Image processing Methodology Normal distribution Optics Pathology Reproducibility Retina Retina blood vessel Statistical model Vision Visual system examination Algorithms Angiography Artifacts Astigmatism Diagnostic Techniques, Ophthalmological Fundus Oculi Humans Image Processing, Computer-Assisted Models, Statistical Normal distribution Optics and Photonics Pattern Recognition, Automated Reproducibility of Results Retina Retinal Vessels Vision, Ocular |
| title_short |
Restoration of retinal images with space-variant blur |
| title_full |
Restoration of retinal images with space-variant blur |
| title_fullStr |
Restoration of retinal images with space-variant blur |
| title_full_unstemmed |
Restoration of retinal images with space-variant blur |
| title_sort |
Restoration of retinal images with space-variant blur |
| dc.subject.keywords.none.fl_str_mv |
Blind deconvolution Deblurring Image restoration Retinal image Space-variant restoration Blind deconvolution Clinical resources Deblurring Innovative strategies Point-spread functions Restoration methods Retinal image Space variants Aberrations Convolution Diagnosis Image reconstruction Ophthalmology Restoration Image enhancement Algorithm Angiography Article Artifact Astigmatism Automated pattern recognition Eye fundus Human Image processing Methodology Normal distribution Optics Pathology Reproducibility Retina Retina blood vessel Statistical model Vision Visual system examination Algorithms Angiography Artifacts Astigmatism Diagnostic Techniques, Ophthalmological Fundus Oculi Humans Image Processing, Computer-Assisted Models, Statistical Normal distribution Optics and Photonics Pattern Recognition, Automated Reproducibility of Results Retina Retinal Vessels Vision, Ocular |
| topic |
Blind deconvolution Deblurring Image restoration Retinal image Space-variant restoration Blind deconvolution Clinical resources Deblurring Innovative strategies Point-spread functions Restoration methods Retinal image Space variants Aberrations Convolution Diagnosis Image reconstruction Ophthalmology Restoration Image enhancement Algorithm Angiography Article Artifact Astigmatism Automated pattern recognition Eye fundus Human Image processing Methodology Normal distribution Optics Pathology Reproducibility Retina Retina blood vessel Statistical model Vision Visual system examination Algorithms Angiography Artifacts Astigmatism Diagnostic Techniques, Ophthalmological Fundus Oculi Humans Image Processing, Computer-Assisted Models, Statistical Normal distribution Optics and Photonics Pattern Recognition, Automated Reproducibility of Results Retina Retinal Vessels Vision, Ocular |
| description |
Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use. © 2014 Society of Photo-Optical Instrumentation Engineers. |
| publishDate |
2014 |
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2014 |
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2020-03-26T16:32:52Z |
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2020-03-26T16:32:52Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Artículo |
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Journal of Biomedical Optics; Vol. 19, Núm. 1 |
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10833668 |
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https://hdl.handle.net/20.500.12585/9066 |
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10.1117/1.JBO.19.1.016023 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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24329839300 7201466399 15846700100 55882243100 |
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Journal of Biomedical Optics; Vol. 19, Núm. 1 10833668 10.1117/1.JBO.19.1.016023 Universidad Tecnológica de Bolívar Repositorio UTB 24329839300 7201466399 15846700100 55882243100 |
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https://hdl.handle.net/20.500.12585/9066 |
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eng |
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2020-03-26T16:32:52Z2020-03-26T16:32:52Z2014Journal of Biomedical Optics; Vol. 19, Núm. 110833668https://hdl.handle.net/20.500.12585/906610.1117/1.JBO.19.1.016023Universidad Tecnológica de BolívarRepositorio UTB2432983930072014663991584670010055882243100Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use. © 2014 Society of Photo-Optical Instrumentation Engineers.Ministerio de Educación, Cultura y Deporte Ministerio de Ciencia e Innovación, MICINN: TEC2010-09834-E, TEC2010-20307, DPI2009-08879 Grantová Agentura České Republiky: 13-29225SThis research has been partly funded by the Spanish Ministerio de Ciencia e Innovación y Fondos FEDER (project DPI2009-08879) and projects TEC2010-09834-E and TEC2010-20307. Financial support was also provided by the Grant Agency of the Czech Republic under project 13-29225S. Authors are grateful to Juan Luís Fuentes from the Miguel Servet University Hospital (Zaragoza, Spain) for providing images. The first author also thanks the Spanish Ministerio de Educación for an FPU doctoral scholarship.Recurso electrónicoapplication/pdfenghttp://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-s2.0-84897748145&doi=10.1117%2f1.JBO.19.1.016023&partnerID=40&md5=1f497b46e4a49bb686336ad515805e62Restoration of retinal images with space-variant blurinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Blind deconvolutionDeblurringImage restorationRetinal imageSpace-variant restorationBlind deconvolutionClinical resourcesDeblurringInnovative strategiesPoint-spread functionsRestoration methodsRetinal imageSpace variantsAberrationsConvolutionDiagnosisImage reconstructionOphthalmologyRestorationImage enhancementAlgorithmAngiographyArticleArtifactAstigmatismAutomated pattern recognitionEye fundusHumanImage processingMethodologyNormal distributionOpticsPathologyReproducibilityRetinaRetina blood vesselStatistical modelVisionVisual system examinationAlgorithmsAngiographyArtifactsAstigmatismDiagnostic Techniques, OphthalmologicalFundus OculiHumansImage Processing, Computer-AssistedModels, StatisticalNormal distributionOptics and PhotonicsPattern Recognition, AutomatedReproducibility of ResultsRetinaRetinal VesselsVision, OcularMarrugo A.G.Millán M.S.Šorel M.Šroubek F.Bartling, H., Wanger, P., Martin, L., Automated quality evaluation of digital fundus photographs (2009) Acta Ophthalmol, 87 (6), pp. 643-647Godara, P., Adaptive optics retinal imaging: Emerging clinical applications (2010) Optom. Vision Sci, 87 (12), pp. 930-941Arines, J., Acosta, E., Low-cost adaptive astigmatism compensator for improvement of eye fundus camera (2011) Opt. Lett, 36 (21), pp. 4164-4166Marrugo, A.G., Retinal image restoration by means of blind deconvolution (2011) J. Biomed. Opt, 16 (11), p. 116016Levin, A., Understanding blind deconvolution algorithms (2011) IEEE Trans. Pattern Anal. Mach. Intell, 33 (12), pp. 2354-2367Bedggood, P., Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging (2008) J. Biomed. Opt, 13 (2), p. 024008Tutt, R., Optical and visual impact of tear break-up in human eyes (2000) Invest. Ophthalmol. Visual Sci, 41 (13), pp. 4117-4123Xu, J., Dynamic changes in ocular zernike aberrations and tear menisci measured with a wavefront sensor and an anterior segment oct (2011) Invest. Ophthalmol. Vis. Sci, 52 (8), pp. 6050-6056Costello, T., Mikhael, W., Efficient restoration of space-variant blurs from physical optics by sectioning with modified wiener filtering (2003) Digital Signal Process, 13 (1), pp. 1-22Bardsley, J., Jefferies, S., Nagy, J., Plemmons, R., A computational method for the restoration of images with an unknown, spatially-varying blur (2006) Optics Express, 14 (5), pp. 1767-1782. , http://www.opticsexpress.org/ViewMedia.cfm?id=88317&seq=0, DOI 10.1364/OE.14.001767Harmeling, S., Hirsch, M., Scholkopf, B., Space-variant singleimage blind deconvolution for removing camera shake (2010) Adv. Neural Inf. Process. Syst, 23, pp. 829-837Whyte, O., Non-uniform deblurring for shaken images (2012) Int. J. Comput. Vis, 98 (2), pp. 168-186Gupta, A., Single image deblurring using motion density functions (2010) European Conf. on Computer Vision (ECCV 2010), pp. 171-184. , K. Daniilidis, P. Maragos, and N. Paragios, Eds., Springer-Verlag, Crete, GreeceTallón, M., Space-variant blur deconvolution and denoising in the dual exposure problem (2012) Inf. Fusion, 14 (4), pp. 396-409Salem, N.M., Nandi, A.K., Novel and adaptive contribution of the red channel in pre-processing of colour fundus images (2007) Journal of the Franklin Institute, 344 (3-4), pp. 243-256. , DOI 10.1016/j.jfranklin.2006.09.001, PII S0016003206001281Marrugo, A.G., Millán, M.S., Retinal image analysis: Preprocessing and feature extraction (2011) J. Phys.: Conf. Ser, 274 (1), p. 012039Golub, G., Van Loan, C., (1996) Matrix Computations, 3. , Johns Hopkins University Press, Baltimore, MarylandNavarro, R., Moreno, E., Dorronsoro, C., Monochromatic aberrations and point-spread functions of the human eye across the visual field (1998) Journal of the Optical Society of America A: Optics and Image Science, and Vision, 15 (9), pp. 2522-2529Sroubek, F., Flusser, J., Multichannel blind deconvolution of spatially misaligned images (2005) IEEE Transactions on Image Processing, 14 (7), pp. 874-883. , DOI 10.1109/TIP.2005.849322Navarro, R., The optical design of the human eye: A critical review (2009) J. 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Res, 4 (3), pp. 315-328Al-Rawi, M., Qutaishat, M., Arrar, M., An improved matched filter for blood vessel detection of digital retinal images (2007) Computers in Biology and Medicine, 37 (2), pp. 262-267. , DOI 10.1016/j.compbiomed.2006.03.003, PII S0010482506000424http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9066/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9066oai:repositorio.utb.edu.co:20.500.12585/90662021-02-02 14:52:42.038Repositorio Institucional UTBrepositorioutb@utb.edu.co |