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Conference Paper: Sparse hierarchical nonparametric Bayesian Learning for light field representation and denoising

TitleSparse hierarchical nonparametric Bayesian Learning for light field representation and denoising
Authors
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500
Citation
The 2016 International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, 24-29 July 2016. In Conference Proceedings, 2016, p. 3272-3279 How to Cite?
AbstractIn this paper, we present a sparse hierarchical non-parametric Bayesian (SHNB) model, which is used to represent the data captured by the light field cameras. Specifically, a light field can be represented as a set of sub-aperture views. In order to capture the visual variations of these viewpoints, we propose the so-called “depth flow” features. Then based on the depth flow features, we model these views statistically with a sparse representation in a fully unsupervised manner. While local dictionaries are learned based on each sub-aperture view, all the views with different perspectives share one global dictionary. To show the effectiveness of the proposed model, we apply our model to denoise the light field data. In the experiments, we demonstrate that our method outperforms several state-of-the-art light field denoising approaches.
DescriptionIEEE WCCI 2016 will host three conferences: The 2016 International Joint Conference on Neural Networks (IJCNN 2016), the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), and the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016)
Persistent Identifierhttp://hdl.handle.net/10722/234986
ISSN

 

DC FieldValueLanguage
dc.contributor.authorSun, X-
dc.contributor.authorMeng, N-
dc.contributor.authorXu, Z-
dc.contributor.authorLam, EYM-
dc.contributor.authorSo, HKH-
dc.date.accessioned2016-10-14T13:50:32Z-
dc.date.available2016-10-14T13:50:32Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, 24-29 July 2016. In Conference Proceedings, 2016, p. 3272-3279-
dc.identifier.issn2161-4407-
dc.identifier.urihttp://hdl.handle.net/10722/234986-
dc.descriptionIEEE WCCI 2016 will host three conferences: The 2016 International Joint Conference on Neural Networks (IJCNN 2016), the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), and the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016)-
dc.description.abstractIn this paper, we present a sparse hierarchical non-parametric Bayesian (SHNB) model, which is used to represent the data captured by the light field cameras. Specifically, a light field can be represented as a set of sub-aperture views. In order to capture the visual variations of these viewpoints, we propose the so-called “depth flow” features. Then based on the depth flow features, we model these views statistically with a sparse representation in a fully unsupervised manner. While local dictionaries are learned based on each sub-aperture view, all the views with different perspectives share one global dictionary. To show the effectiveness of the proposed model, we apply our model to denoise the light field data. In the experiments, we demonstrate that our method outperforms several state-of-the-art light field denoising approaches.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500-
dc.relation.ispartofInternational Joint Conference on Neural Networks (IJCNN) Proceedings-
dc.rightsInternational Joint Conference on Neural Networks (IJCNN) Proceedings. Copyright © IEEE.-
dc.rights©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleSparse hierarchical nonparametric Bayesian Learning for light field representation and denoising-
dc.typeConference_Paper-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.emailSo, HKH: hso@eee.hku.hk-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.authoritySo, HKH=rp00169-
dc.identifier.doi10.1109/IJCNN.2016.7727617-
dc.identifier.hkuros268708-
dc.identifier.spage3272-
dc.identifier.epage3279-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161202-

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