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dc.contributor.authorLi, Muxingzi
dc.contributor.authorIdoughi, Ramzi
dc.contributor.authorChoudhury, Biswarup
dc.contributor.authorHeidrich, Wolfgang
dc.date.accessioned2017-10-03T12:49:35Z
dc.date.available2017-10-03T12:49:35Z
dc.date.issued2017-08-01
dc.identifier.citationLi M, Idoughi R, Choudhury B, Heidrich W (2017) Statistical model for OCT image denoising. Biomedical Optics Express 8: 3903. Available: http://dx.doi.org/10.1364/boe.8.003903.
dc.identifier.issn2156-7085
dc.identifier.issn2156-7085
dc.identifier.doi10.1364/boe.8.003903
dc.identifier.urihttp://hdl.handle.net/10754/625719
dc.description.abstractOptical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.
dc.description.sponsorshipKing Abdullah University of Science and Technology (KAUST) (Visual Computing Center Competitive Funding). We would like to thank Prof. TorOve Leiknes and Luca Fortunato of Water Desalination and Reuse Center, KAUST for help with the OCT data acquisition. We also take this opportunity to thank the ThorLabs, Munich engineering team for their assistance with the native OCT file handling procedures. The authors would also like to thank Mohamed Aly for his valuable feedback on the project.
dc.publisherThe Optical Society
dc.relation.urlhttps://www.osapublishing.org/boe/abstract.cfm?uri=boe-8-9-3903
dc.relation.urlhttp://europepmc.org/articles/pmc5611912?pdf=render
dc.rightsThis is an open access article.
dc.rightsThis file is an open access version redistributed from: http://europepmc.org/articles/pmc5611912?pdf=render
dc.subjectImage enhancement
dc.subjectNoise in imaging systems
dc.subjectOptical coherence tomography
dc.subjectSpeckle
dc.subjectStatistical optics
dc.titleStatistical model for OCT image denoising
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer Science Program
dc.identifier.journalBiomedical Optics Express
dc.rights.embargodate2018-09-01
dc.eprint.versionPublisher's Version/PDF
kaust.personLi, Muxingzi
kaust.personIdoughi, Ramzi
kaust.personChoudhury, Biswarup
kaust.personHeidrich, Wolfgang
refterms.dateFOA2020-08-20T12:51:37Z
dc.date.published-online2017-08-01
dc.date.published-print2017-09-01


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