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 |