Single face image super-resolution via multi-dictionary bayesian non-parametric learning

Jingjing Wu; Hua Zhang; Yanbing Xue; Mian Zhou; Guangping Xu; Zan Gao

doi:10.1007/978-3-319-26532-2_59

Single face image super-resolution via multi-dictionary bayesian non-parametric learning

Jingjing Wu, Hua Zhang, Yanbing Xue^*, Mian Zhou, Guangping Xu, Zan Gao

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.

Original language	English
Title of host publication	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Editors	Weng Kin Lai, Qingshan Liu, Tingwen Huang, Sabri Arik
Publisher	Springer Verlag
Pages	540-548
Number of pages	9
ISBN (Print)	9783319265315
DOIs	https://doi.org/10.1007/978-3-319-26532-2_59
Publication status	Published - 2015
Externally published	Yes
Event	22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9489
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Neural Information Processing, ICONIP 2015
Country/Territory	Turkey
City	Istanbul
Period	9/11/15 → 12/11/15

Keywords

Beta process
Multi-dictionary
Pre-clustering
Super-resolution

Access to Document

10.1007/978-3-319-26532-2_59

Cite this

Wu, J., Zhang, H., Xue, Y., Zhou, M., Xu, G., & Gao, Z. (2015). Single face image super-resolution via multi-dictionary bayesian non-parametric learning. In W. K. Lai, Q. Liu, T. Huang, & S. Arik (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings (pp. 540-548). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_59

Wu, Jingjing ; Zhang, Hua ; Xue, Yanbing et al. / Single face image super-resolution via multi-dictionary bayesian non-parametric learning. Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. editor / Weng Kin Lai ; Qingshan Liu ; Tingwen Huang ; Sabri Arik. Springer Verlag, 2015. pp. 540-548 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Single face image super-resolution via multi-dictionary bayesian non-parametric learning",

abstract = "The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.",

keywords = "Beta process, Multi-dictionary, Pre-clustering, Super-resolution",

author = "Jingjing Wu and Hua Zhang and Yanbing Xue and Mian Zhou and Guangping Xu and Zan Gao",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

doi = "10.1007/978-3-319-26532-2_59",

language = "English",

isbn = "9783319265315",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Wu, J, Zhang, H, Xue, Y, Zhou, M, Xu, G & Gao, Z 2015, Single face image super-resolution via multi-dictionary bayesian non-parametric learning. in WK Lai, Q Liu, T Huang & S Arik (eds), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9489, Springer Verlag, pp. 540-548, 22nd International Conference on Neural Information Processing, ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26532-2_59

Single face image super-resolution via multi-dictionary bayesian non-parametric learning. / Wu, Jingjing; Zhang, Hua; Xue, Yanbing et al.
Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. ed. / Weng Kin Lai; Qingshan Liu; Tingwen Huang; Sabri Arik. Springer Verlag, 2015. p. 540-548 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Single face image super-resolution via multi-dictionary bayesian non-parametric learning

AU - Wu, Jingjing

AU - Zhang, Hua

AU - Xue, Yanbing

AU - Zhou, Mian

AU - Xu, Guangping

AU - Gao, Zan

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.

AB - The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.

KW - Beta process

KW - Multi-dictionary

KW - Pre-clustering

KW - Super-resolution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings

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Wu J, Zhang H, Xue Y, Zhou M, Xu G, Gao Z. Single face image super-resolution via multi-dictionary bayesian non-parametric learning. In Lai WK, Liu Q, Huang T, Arik S, editors, Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Springer Verlag. 2015. p. 540-548. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26532-2_59

Single face image super-resolution via multi-dictionary bayesian non-parametric learning

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