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dc.contributor.authorZhang, Wen
dc.date.accessioned2010-01-13 17:58:22 (GMT)
dc.date.available2010-01-13 17:58:22 (GMT)
dc.date.issued2010-01-13T17:58:22Z
dc.date.submitted2010
dc.identifier.urihttp://hdl.handle.net/10012/4920
dc.description.abstractImage noise reduction, or denoising, is an active area of research, although many of the techniques cited in the literature mainly target additive white noise. With an emphasis on signal-dependent noise, this thesis presents the General Adaptive Monte Carlo Bayesian Image Denoising (GAMBID) algorithm, a model-free approach based on random sampling. Testing is conducted on synthetic images with two different signal-dependent noise types as well as on real synthetic aperture radar and ultrasound images. Results show that GAMBID can achieve state-of-the-art performance, but suffers from some limitations in dealing with textures and fine low-contrast features. These aspects can by addressed in future iterations when GAMBID is expanded to become a versatile denoising framework.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectimage processingen
dc.subjectnoise reductionen
dc.subjectMonte Carlo methodsen
dc.subjectmultiplicative noiseen
dc.subjectBayesian estimationen
dc.subjectspeckleen
dc.titleGeneral Adaptive Monte Carlo Bayesian Image Denoisingen
dc.typeMaster Thesisen
dc.pendingfalseen
dc.subject.programSystem Design Engineeringen
uws-etd.degree.departmentSystems Design Engineeringen
uws-etd.degreeMaster of Applied Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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