Multi-Models Fusion for Light Field Angular Super-Resolution

Cao, F; An, P; Huang, X; Yang, C; Wu, Q

Multi-Models Fusion for Light Field Angular Super-Resolution

Cao, F An, P Huang, X Yang, C Wu, Q

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, 2021-June, pp. 2365-2369
Issue Date:: 2021-05-13

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Field	Value	Language
dc.contributor.author	Cao, F
dc.contributor.author	An, P
dc.contributor.author	Huang, X
dc.contributor.author	Yang, C
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.date	2021-06-06
dc.date.accessioned	2022-06-11T00:34:50Z
dc.date.available	2022-06-11T00:34:50Z
dc.date.issued	2021-05-13
dc.identifier.citation	ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, 2021-June, pp. 2365-2369
dc.identifier.issn	1520-6149
dc.identifier.uri	http://hdl.handle.net/10453/158092
dc.description.abstract	Light field (LF) imaging has received increasing attention due to its richer interpretation of the scene. However, an inherent spatial-angular trade-off exists in LF that prevents LF from practical applications. Consequently, how to break such a trade-off has become one of the main challenges in sparsely sampled LF reconstruction. LF super-resolution (SR) can provide an opportunity to solve this issue, but most methods exploit only one form of LF, thereby leading to much loss of information. We believe that different LF forms can compensate each other to obtain higher gains via fusion strategy. In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. Cascading models which are trained by different LF forms can fully exploit rich LF information. Experimental results demonstrate that our method is effective and achieves a comparable result against state-of-the-art techniques.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.relation.ispartof	ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing
dc.relation.isbasedon	10.1109/icassp39728.2021.9413824
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multi-Models Fusion for Light Field Angular Super-Resolution
dc.type	Conference Proceeding
utslib.citation.volume	2021-June
utslib.location.activity	Toronto, ON, Canada
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/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-11T00:34:48Z
pubs.finish-date	2021-06-11
pubs.publication-status	Published
pubs.start-date	2021-06-06
pubs.volume	2021-June

Abstract:

Light field (LF) imaging has received increasing attention due to its richer interpretation of the scene. However, an inherent spatial-angular trade-off exists in LF that prevents LF from practical applications. Consequently, how to break such a trade-off has become one of the main challenges in sparsely sampled LF reconstruction. LF super-resolution (SR) can provide an opportunity to solve this issue, but most methods exploit only one form of LF, thereby leading to much loss of information. We believe that different LF forms can compensate each other to obtain higher gains via fusion strategy. In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. Cascading models which are trained by different LF forms can fully exploit rich LF information. Experimental results demonstrate that our method is effective and achieves a comparable result against state-of-the-art techniques.

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http://hdl.handle.net/10453/158092