Light field spatial and angular super-resolution
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Wafa, Abrar
issued: 2024-01-31T08:00:00Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Light field technology offers a truly immersive experience, having the potential to revolutionize entertainment, education, virtual and augmented reality, autonomous driving, and digital health. There are two common techniques for capturing LF: multiple-camera arrays and microlens array (i.e., plenoptic camera). However, LF capturing techniques face a trade-off between spatial and angular resolution. That is, camera arrays capture high-spatial-resolution LF images with sparse angular sampling (i.e., fewer views), while plenoptic cameras capture dense angular sampling (i.e., more views) with low-spatial-resolution LF images due to the size of the plenoptic camera sensors. Since constructing an LF camera array is expensive, time consuming, and often impractical, many efforts have been made to improve the spatial resolution of LF images captured by plenoptic cameras. In this thesis, we propose two novel methods for LF spatial super-resolution (SR). First, we propose a learning-based model for spatial SR which takes advantage of the epipolar image plane (EPI) information to ensure smooth disparity between the generated views and in turn construct high-spatial-resolution LF images. In our second contribution, we exploit the full four-dimensional (4D) LF images by proposing a deep-learning spatial SR approach that considers the spatial and angular information (i.e., information within each view and information among other views) and progressively reconstructs high-resolution LF images at different upscaling levels. Another challenge when dealing with LF images is the enormous amount of data generated, as they require significant increase in bandwidth. A possible solution may be to drop specific views at the transmitting end and effectively synthesize them at the receiver end, thus minimizing the amount of data needed to be transferred or stored. Accordingly, our third contribution focuses on LF angular SR by synthesizing virtual LF views from a sparse set of input views u…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83408
note: LIGHT FIELD SPATIAL AND ANGULAR SUPER-RESOLUTION by Abrar Wafa B.S., Effat University, 2012 M.A.Sc, University of British Columbia, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Abrar Wafa, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: LIGHT FIELD SPATIAL AND ANGULAR SUPER-RESOLUTION submitted by Abrar Wafa in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering Examining Committee: Panos Nasiopoulos, Professor, Electrical and Computer Engineering Department, UBC Supervisor Victor Leung, Professor, Electrical and Computer Engineering Department, UBC Supervisory Committee Member Matthew Yedlin, Professor, Electrical and Computer Engineering Department, UBC Supervisory Committee Member Shahriar Mirabbasi, Professor, Electrical and Computer Engineering Department, UBC University Examiner Jim Little, Professor, Computer Science Department, UBC University Examiner iii Abstract Light field technology offers a truly immersive experience, having the potential to revolutionize entertainment, education, virtual and augmented reality, autonomous driving, and digital health. There are two common techniques for capturing LF: multiple-camera arrays and microlens array (i.e., plenoptic camera). However, LF capturing techniques face a trade-off between spatial and angular resolution. That is, camera arrays capture high-spatial-resolution LF images with sparse angular sampling (i.e., fewer views), while plenoptic cameras capture dense angular sampling (i.e., more views) with low-spatial-resolution LF images due to the size of the plenoptic camera sensors. Since constructing an LF camera …
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0422497
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Nasiopoulos, Panos
type: Text
identifier URI: http://hdl.handle.net/2429/83408