IEICE TRANSACTIONS on Information
Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition
Summary :
A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.9 pp.2190-2200
- Publication Date
- 2018/09/01
- Publicized
- 2018/06/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017PCP0007
- Type of Manuscript
- Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Yusuke YAGI
Nagoya University
Keita TAKAHASHI
Nagoya University
Toshiaki FUJII
Nagoya University
Toshiki SONODA
Kyushu University
Hajime NAGAHARA
Osaka University
Keyword
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Yusuke YAGI, Keita TAKAHASHI, Toshiaki FUJII, Toshiki SONODA, Hajime NAGAHARA, "Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2190-2200, September 2018, doi: 10.1587/transinf.2017PCP0007.
Abstract: A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017PCP0007/_p
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@ARTICLE{e101-d_9_2190,
author={Yusuke YAGI, Keita TAKAHASHI, Toshiaki FUJII, Toshiki SONODA, Hajime NAGAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition},
year={2018},
volume={E101-D},
number={9},
pages={2190-2200},
abstract={A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.},
keywords={},
doi={10.1587/transinf.2017PCP0007},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Designing Coded Aperture Camera Based on PCA and NMF for Light Field Acquisition
T2 - IEICE TRANSACTIONS on Information
SP - 2190
EP - 2200
AU - Yusuke YAGI
AU - Keita TAKAHASHI
AU - Toshiaki FUJII
AU - Toshiki SONODA
AU - Hajime NAGAHARA
PY - 2018
DO - 10.1587/transinf.2017PCP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.
ER -
English
