Resumé

Super-resolution algorithms are used to improve the qualityand resolution of low-resolution images. These algorithms can be dividedinto two classes of hallucination- and reconstruction-based ones. Theimprovement factors of these algorithms are limited, however, previousresearch [10], [9] has shown that combining super-resolution algorithmsfrom these two different groups can push the improvement factor further.We have shown in this paper that combining super-resolution algorithmsof the same class can also push the improvement factor up. For thispurpose, we have combined two hallucination based algorithms, namelythe one found in Single Image Super-Resolution from Transformed Self-Exemplars [7] and the Super-Resolution Convolutional Neural Networkfrom [4]. The combination of these two, through an alpha-blending, hasresulted in a system that outperforms state-of-the-art super-resolutionalgorithms on public benchmark datasets.
OriginalsprogEngelsk
TitelVideo Analytics : Face and Facial Expression Recognition and Audience Measurement
ForlagSpringer
Publikationsdato28 mar. 2017
ISBN (Trykt)978-3-319-56686-3
ISBN (Elektronisk)978-3-319-56687-0
DOI
StatusUdgivet - 28 mar. 2017
Begivenhed23rd international conference on pattern recognition (ICPR 2016) - Cancun, Mexico
Varighed: 4 dec. 20168 dec. 2016

Konference

Konference23rd international conference on pattern recognition (ICPR 2016)
LandMexico
ByCancun
Periode04/12/201608/12/2016
NavnLecture Notes in Computer Science
Vol/bind10165
ISSN0302-9743

Fingerprint

Optical resolving power
Image resolution

Citer dette

Aakerberg, A., Rasmussen, C. B., Nasrollahi, K., & Moeslund, T. B. (2017). Complementing SRCNN by Transformed Self-Exemplars. I Video Analytics: Face and Facial Expression Recognition and Audience Measurement Springer. Lecture Notes in Computer Science, Bind. 10165 https://doi.org/10.1007/978-3-319-56687-0_11
Aakerberg, Andreas ; Rasmussen, Christoffer Bøgelund ; Nasrollahi, Kamal ; Moeslund, Thomas B. / Complementing SRCNN by Transformed Self-Exemplars. Video Analytics: Face and Facial Expression Recognition and Audience Measurement. Springer, 2017. (Lecture Notes in Computer Science, Bind 10165).
@inproceedings{cd593f5504804669a755a03962086e89,
title = "Complementing SRCNN by Transformed Self-Exemplars",
abstract = "Super-resolution algorithms are used to improve the qualityand resolution of low-resolution images. These algorithms can be dividedinto two classes of hallucination- and reconstruction-based ones. Theimprovement factors of these algorithms are limited, however, previousresearch [10], [9] has shown that combining super-resolution algorithmsfrom these two different groups can push the improvement factor further.We have shown in this paper that combining super-resolution algorithmsof the same class can also push the improvement factor up. For thispurpose, we have combined two hallucination based algorithms, namelythe one found in Single Image Super-Resolution from Transformed Self-Exemplars [7] and the Super-Resolution Convolutional Neural Networkfrom [4]. The combination of these two, through an alpha-blending, hasresulted in a system that outperforms state-of-the-art super-resolutionalgorithms on public benchmark datasets.",
author = "Andreas Aakerberg and Rasmussen, {Christoffer B{\o}gelund} and Kamal Nasrollahi and Moeslund, {Thomas B.}",
year = "2017",
month = "3",
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doi = "10.1007/978-3-319-56687-0_11",
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series = "Lecture Notes in Computer Science",
publisher = "Springer",
booktitle = "Video Analytics",
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}

Aakerberg, A, Rasmussen, CB, Nasrollahi, K & Moeslund, TB 2017, Complementing SRCNN by Transformed Self-Exemplars. i Video Analytics: Face and Facial Expression Recognition and Audience Measurement. Springer, Lecture Notes in Computer Science, bind 10165, Cancun, Mexico, 04/12/2016. https://doi.org/10.1007/978-3-319-56687-0_11

Complementing SRCNN by Transformed Self-Exemplars. / Aakerberg, Andreas; Rasmussen, Christoffer Bøgelund; Nasrollahi, Kamal; Moeslund, Thomas B.

Video Analytics: Face and Facial Expression Recognition and Audience Measurement. Springer, 2017. (Lecture Notes in Computer Science, Bind 10165).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Complementing SRCNN by Transformed Self-Exemplars

AU - Aakerberg, Andreas

AU - Rasmussen, Christoffer Bøgelund

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

PY - 2017/3/28

Y1 - 2017/3/28

N2 - Super-resolution algorithms are used to improve the qualityand resolution of low-resolution images. These algorithms can be dividedinto two classes of hallucination- and reconstruction-based ones. Theimprovement factors of these algorithms are limited, however, previousresearch [10], [9] has shown that combining super-resolution algorithmsfrom these two different groups can push the improvement factor further.We have shown in this paper that combining super-resolution algorithmsof the same class can also push the improvement factor up. For thispurpose, we have combined two hallucination based algorithms, namelythe one found in Single Image Super-Resolution from Transformed Self-Exemplars [7] and the Super-Resolution Convolutional Neural Networkfrom [4]. The combination of these two, through an alpha-blending, hasresulted in a system that outperforms state-of-the-art super-resolutionalgorithms on public benchmark datasets.

AB - Super-resolution algorithms are used to improve the qualityand resolution of low-resolution images. These algorithms can be dividedinto two classes of hallucination- and reconstruction-based ones. Theimprovement factors of these algorithms are limited, however, previousresearch [10], [9] has shown that combining super-resolution algorithmsfrom these two different groups can push the improvement factor further.We have shown in this paper that combining super-resolution algorithmsof the same class can also push the improvement factor up. For thispurpose, we have combined two hallucination based algorithms, namelythe one found in Single Image Super-Resolution from Transformed Self-Exemplars [7] and the Super-Resolution Convolutional Neural Networkfrom [4]. The combination of these two, through an alpha-blending, hasresulted in a system that outperforms state-of-the-art super-resolutionalgorithms on public benchmark datasets.

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DO - 10.1007/978-3-319-56687-0_11

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SN - 978-3-319-56686-3

T3 - Lecture Notes in Computer Science

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Aakerberg A, Rasmussen CB, Nasrollahi K, Moeslund TB. Complementing SRCNN by Transformed Self-Exemplars. I Video Analytics: Face and Facial Expression Recognition and Audience Measurement. Springer. 2017. (Lecture Notes in Computer Science, Bind 10165). https://doi.org/10.1007/978-3-319-56687-0_11