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Computational Large Field-of-View RGB-D Integral Imaging System

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each le...

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Autores principales: Jung, Geunho, Won, Yong-Yuk, Yoon, Sang Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587062/
https://www.ncbi.nlm.nih.gov/pubmed/34770713
http://dx.doi.org/10.3390/s21217407
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author Jung, Geunho
Won, Yong-Yuk
Yoon, Sang Min
author_facet Jung, Geunho
Won, Yong-Yuk
Yoon, Sang Min
author_sort Jung, Geunho
collection PubMed
description The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.
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spelling pubmed-85870622021-11-13 Computational Large Field-of-View RGB-D Integral Imaging System Jung, Geunho Won, Yong-Yuk Yoon, Sang Min Sensors (Basel) Article The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness. MDPI 2021-11-08 /pmc/articles/PMC8587062/ /pubmed/34770713 http://dx.doi.org/10.3390/s21217407 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jung, Geunho
Won, Yong-Yuk
Yoon, Sang Min
Computational Large Field-of-View RGB-D Integral Imaging System
title Computational Large Field-of-View RGB-D Integral Imaging System
title_full Computational Large Field-of-View RGB-D Integral Imaging System
title_fullStr Computational Large Field-of-View RGB-D Integral Imaging System
title_full_unstemmed Computational Large Field-of-View RGB-D Integral Imaging System
title_short Computational Large Field-of-View RGB-D Integral Imaging System
title_sort computational large field-of-view rgb-d integral imaging system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587062/
https://www.ncbi.nlm.nih.gov/pubmed/34770713
http://dx.doi.org/10.3390/s21217407
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