| Field |
Value |
Language |
|
dc.contributor.author |
Sharma, R |
|
|
dc.contributor.author |
Perry, S
https://orcid.org/0000-0002-2794-3178
|
|
|
dc.contributor.author |
Cheng, E
https://orcid.org/0000-0003-1632-8062
|
|
|
dc.date.accessioned |
2023-03-27T10:15:59Z |
|
|
dc.date.available |
2023-03-27T10:15:59Z |
|
|
dc.identifier.citation |
Sensors, 23, (4), pp. 2119-2119 |
|
|
dc.identifier.issn |
1424-8220 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/168611
|
|
|
dc.description.abstract |
<jats:p>Light field reconstruction and synthesis algorithms are essential for improving the lower spatial resolution for hand-held plenoptic cameras. Previous light field synthesis algorithms produce blurred regions around depth discontinuities, especially for stereo-based algorithms, where no information is available to fill the occluded areas in the light field image. In this paper, we propose a light field synthesis algorithm that uses the focal stack images and the all-in-focus image to synthesize a 9 × 9 sub-aperture view light field image. Our approach uses depth from defocus to estimate a depth map. Then, we use the depth map and the all-in-focus image to synthesize the sub-aperture views, and their corresponding depth maps by mimicking the apparent shifting of the central image according to the depth values. We handle the occluded regions in the synthesized sub-aperture views by filling them with the information recovered from the focal stack images. We also show that, if the depth levels in the image are known, we can synthesize a high-accuracy light field image with just five focal stack images. The accuracy of our approach is compared with three state-of-the-art algorithms: one non-learning and two CNN-based approaches, and the results show that our algorithm outperforms all three in terms of PSNR and SSIM metrics.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
MDPI AG |
|
|
dc.relation.ispartof |
Sensors |
|
|
dc.relation.isbasedon |
10.3390/s23042119 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering |
|
|
dc.subject.classification |
Analytical Chemistry |
|
|
dc.title |
Light Field View Synthesis Using the Focal Stack and All-in-Focus Image |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
23 |
|
|
utslib.for |
0301 Analytical Chemistry |
|
|
utslib.for |
0502 Environmental Science and Management |
|
|
utslib.for |
0602 Ecology |
|
|
utslib.for |
0805 Distributed Computing |
|
|
utslib.for |
0906 Electrical and Electronic Engineering |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2023-03-27T10:15:31Z |
|
|
pubs.issue |
4 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
23 |
|
|
utslib.citation.issue |
4 |
|
