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Efficient methodologies for real-time image restoration

Samarasinghe, Devanarayanage Pradeepa

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In this thesis we investigate the problem of image restoration. The main focus of our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent...[Show more]

dc.contributor.authorSamarasinghe, Devanarayanage Pradeepa
dc.date.accessioned2013-04-18T23:42:49Z
dc.date.available2013-04-18T23:42:49Z
dc.identifier.otherb28789350
dc.identifier.urihttp://hdl.handle.net/1885/9859
dc.description.abstractIn this thesis we investigate the problem of image restoration. The main focus of our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent developments in the area of image restoration. With this novel classification, we identified three major areas which need our attention. The first developments relate to non-blind image restoration. The two mostly used techniques, namely deterministic linear algorithms and stochastic nonlinear algorithms are compared and contrasted. Under deterministic linear algorithms, we develop a class of more effective novel quadratic linear regularization models, which outperform the existing linear regularization models. In addition, by looking in a new perspective, we evaluate and compare the performance of deterministic and stochastic restoration algorithms and explore the validity of the performance claims made so far on those algorithms. Further, we critically challenge the ne- cessity of some complex mechanisms in Maximum A Posteriori (MAP) technique under stochastic image deconvolution algorithms. The next developments are focussed in blind image restoration, which is claimed to be more challenging. Constant Modulus Algorithm (CMA) is one of the most popular, computationally simple, tested and best performing blind equalization algorithms in the signal processing domain. In our research, we extend the use of CMA in image restoration and develop a broad class of blind image deconvolution algorithms, in particular algorithms for blurring kernels with a separable property. These algorithms show significantly faster convergence than conventional algorithms. Although CMA method has a proven record in signal processing applications related to data communications systems, no research has been carried out to the investigation of the applicability of CMA for image restoration in practice. In filling this gap and taking into account the differences of signal processing in im- age processing and data communications contexts, we extend our research on the applicability of CMA deconvolution under the assumptions on the ground truth image properties. Through analyzing the main assumptions of ground truth image properties being zero-mean, independent and uniformly distributed, which char- acterize the convergence of CMA deconvolution, we develop a novel technique to overcome the effects of image source correlation based on segmentation and higher order moments of the source. Multichannel image restoration techniques recently gained much attention over the single channel image restoration due to the benefits of diversity and redundancy of the information between the channels. Exploiting these benefits in real time applications is often restricted due to the unavailability of multiple copies of the same image. In order to overcome this limitation, as the last area of our research, we develop a novel multichannel blind restoration model with a single image, which eliminates the constraint of the necessity of multiple copies of the blurred image. We consider this as a major contribution which could be extended to wider areas of research integrated with multiple disciplines such as demosaicing.
dc.language.isoen_AU
dc.subjectblind adaptation
dc.subjectdeconvolution
dc.subjectimage processing
dc.subjectblind image deconvolution
dc.subjectConstant Modulus Algorithm
dc.subjectCMA
dc.subjectgradient descent
dc.subjectseparable kernels
dc.subjectblind equalization
dc.subjectblind deconvolution
dc.subjectblind image restoration
dc.subjectGodard algorithm
dc.subjectkurtosis
dc.subjectnon-blind image restoration
dc.subjectmaximum a posteriori (MAP) framework
dc.subjectlikelihood
dc.subjectground truth prior
dc.subjectfrequency domain deconvolution
dc.subjectmulti channel restoration
dc.titleEfficient methodologies for real-time image restoration
dc.typeThesis (PhD)
local.contributor.supervisorKennedy, Rodney
local.contributor.supervisorcontactRodney.Kennedy@anu.edu.au
dcterms.valid2011
local.description.notesSupervisor: Professor Rodney Kennedy, Supervisor's Email Address: Rodney.Kennedy@anu.edu.au
local.description.refereedYes
local.type.degreeDoctor of Philosophy (PhD)
dc.date.issued2011
local.contributor.affiliationCollege of Engineering & Computer Science
local.identifier.doi10.25911/5d78d97dd476c
local.mintdoimint
CollectionsOpen Access Theses

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02Whole_Samarasinghe.pdfWhole Thesis4.61 MBAdobe PDFThumbnail


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