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Low-Complexity Multi-Dimensional Filters for Plenoptic Signal ProcessingEdussooriya, Chamira Udaya Shantha 02 December 2015 (has links)
Five-dimensional (5-D) light field video (LFV) (also known as plenoptic video) is a
more powerful form of representing information of dynamic scenes compared to conventional
three-dimensional (3-D) video. In this dissertation, the spectra of moving
objects in LFVs are analyzed, and it is shown that such moving objects can be enhanced
based on their depth and velocity by employing 5-D digital filters, what is
defined as depth-velocity filters. In particular, the spectral region of support (ROS)
of a Lambertian object moving with constant velocity and at constant depth is shown
to be a skewed 3-D hyperfan in the 5-D frequency domain. Furthermore, it is shown
that the spectral ROS of a Lambertian object moving at non-constant depth can be
approximated as a sequence of ROSs, each of which is a skewed 3-D hyperfan, in the
5-D continuous frequency domain.
Based on the spectral analysis, a novel 5-D finite-extent impulse response (FIR)
depth-velocity filter and a novel ultra-low complexity 5-D infinite-extent impulse response
(IIR) depth-velocity filter are proposed for enhancing objects moving with
constant velocity and at constant depth in LFVs. Furthermore, a novel ultra-low
complexity 5-D IIR adaptive depth-velocity filter is proposed for enhancing objects
moving at non-constant depth in LFVs. Also, an ultra-low complexity 3-D linear-phase
IIR velocity filter that can be incorporated to design 5-D IIR depth-velocity
filters is proposed. To the best of the author’s knowledge, the proposed 5-D FIR and
IIR depth-velocity filters and the proposed 5-D IIR adaptive depth-velocity filter are
the first such 5-D filters applied for enhancing moving objects in LFVs based on their
depth and velocity.
Numerically generated LFVs and LFVs of real scenes, generated by means of a
commercially available Lytro light field (LF) camera, are used to test the effectiveness
of the proposed 5-D depth-velocity filters. Numerical simulation results indicate that
the proposed 5-D depth-velocity filters outperform the 3-D velocity filters and the
four-dimensional (4-D) depth filters in enhancing moving objects in LFVs. More
importantly, the proposed 5-D depth-velocity filters are capable of exposing heavily
occluded parts of a scene and of attenuating noise significantly. Considering the ultra-low
complexity, the proposed 5-D IIR depth-velocity filter and the proposed 5-D IIR
adaptive depth-velocity filter have significant potentials to be employed in real-time
applications. / Graduate / 0544
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