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Four- dimensional sparse filters for near real-time light field processing

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dc.contributor.advisor Edussooriya CUS
dc.contributor.author Premaratne IWASU
dc.date.accessioned 2019
dc.date.available 2019
dc.date.issued 2019
dc.identifier.citation Premaratne, I.W.A.S.U. (2019). Four- dimensional sparse filters for near real-time light field processing [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.mrt.ac.lk/handle/123/16048
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/16048
dc.description.abstract Light is a fundamental form of conveying information. Sensing of light through conventional cameras leads to images and videos. In contrast to conventional images and videos, which capture only the directional variation of the intensity of light rays emanating from a scene, light fields capture the spatial variation as well. This richness of information has been exploited to accomplish novel tasks that are not possible with conventional images and videos, such as post-capture digital refocusing and depth filtering. As a result of the massive data volume captured by a light field, the light field processing algorithms require higher memory and computational requirement. This is a major drawback for employing light fields in real-time applications. Hence, there is a need for investigating novel low-complexity light field processing algorithms that can be implemented in real-time applications. In this study, we address this critical research problem using multidimensional linear filter theory to develop novel low-complexity and sparse filters for light field processing. To this end, the work presented in this thesis focus on two major scenarios; light field denoising and volumetric refocusing. First, we present a novel low-complexity light field denoising algorithm, utilizing the sparsity of the region of support of a light field in the frequency domain. It turns out that the proposed filter runs in near real-time, compared to the previously reported light field denoising methods which take minutes. Next, a 4-D sparse filter for volumetric refocusing is presented. The proposed sparse filter provides 72% reduction of computational complexity compared to a non-sparse filter, with negligible distortion in fidelity. en_US
dc.language.iso en en_US
dc.subject ELECTRONIC AND TELECOMMUNICATION ENGINEERING-Dissertations en_US
dc.subject LIGHT FIELD IMAGING en_US
dc.subject OPTICAL FILTERS en_US
dc.subject LIGHT FIELD PROCESSING en_US
dc.title Four- dimensional sparse filters for near real-time light field processing en_US
dc.type Thesis-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree Master of Philosophy en_US
dc.identifier.department Department of Electronics & Telecommunication Engineering en_US
dc.date.accept 2019
dc.identifier.accno TH3904 en_US


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