Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images

Salvador Villena; Miguel Vega; Rafael Molina; Aggelos K. Katsaggelos

doi:10.1007/978-3-642-04697-1_18

Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images

Salvador Villena^*, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods.

Original language	English (US)
Title of host publication	Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings
Pages	188-199
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-04697-1_18
State	Published - 2009
Event	11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009 - Bordeaux, France Duration: Sep 28 2009 → Oct 2 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5807 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009
Country/Territory	France
City	Bordeaux
Period	9/28/09 → 10/2/09

Keywords

Bayesian paradigm
Covariance matrix calculation
High resolution images
Variational inference

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-04697-1_18

Cite this

Villena, S., Vega, M., Molina, R., & Katsaggelos, A. K. (2009). Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images. In Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings (pp. 188-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5807 LNCS). https://doi.org/10.1007/978-3-642-04697-1_18

Villena, Salvador ; Vega, Miguel ; Molina, Rafael et al. / Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images. Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings. 2009. pp. 188-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{18887a6fb1c24edca1cc86409c14572c,

title = "Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images",

abstract = "This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods.",

keywords = "Bayesian paradigm, Covariance matrix calculation, High resolution images, Variational inference",

author = "Salvador Villena and Miguel Vega and Rafael Molina and Katsaggelos, {Aggelos K.}",

year = "2009",

doi = "10.1007/978-3-642-04697-1_18",

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

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booktitle = "Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings",

note = "11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009 ; Conference date: 28-09-2009 Through 02-10-2009",

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Villena, S, Vega, M, Molina, R & Katsaggelos, AK 2009, Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images. in Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5807 LNCS, pp. 188-199, 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009, Bordeaux, France, 9/28/09. https://doi.org/10.1007/978-3-642-04697-1_18

Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images. / Villena, Salvador; Vega, Miguel; Molina, Rafael et al.

Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings. 2009. p. 188-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5807 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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N2 - This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods.

AB - This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods.

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KW - Covariance matrix calculation

KW - High resolution images

KW - Variational inference

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Villena S, Vega M, Molina R, Katsaggelos AK. Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images. In Advanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings. 2009. p. 188-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04697-1_18

Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images

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Keywords

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