Improved Interpolation Kernels for Super-resolution Algorithms

Pejman Rasti, Olga Orlova, Gert Tamberg, Cagri Ozcinar, Kamal Nasrollahi, Thomas B. Moeslund, Gholamreza Anbarjafari

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

6 Citationer (Scopus)

Resumé

Super resolution (SR) algorithms are widely used in forensics investigations to enhance the resolution of images captured by surveillance cameras. Such algorithms usually use a common interpolation algorithm to generate an initial guess for the desired high resolution (HR) image. This initial guess is usually tuned through different methods, like learning-based or fusion-based methods, to converge the initial guess towards the desired HR output. In this work, it is shown that SR algorithms can result in better performance if more sophisticated kernels than the simple conventional ones are used for producing the initial guess. The contribution of this work is to introduce such a set of kernels which can be used in the context of SR. The quantitative and qualitative results on many natural, facial and iris images show the superiority of the generated HR images over two state-of-the-art SR algorithms when their original interpolation kernel is replaced by the ones introduced in this work.
OriginalsprogEngelsk
TitelIEEE International Conference on Image Processing Theory, Tools and Applications
ForlagIEEE
Publikationsdatodec. 2016
ISBN (Trykt)978-1-4673-8911-2
ISBN (Elektronisk)978-1-4673-8910-5
DOI
StatusUdgivet - dec. 2016
BegivenhedIEEE International Conference on Image Processing Theory, Tools and Applications - Oulu, Finland
Varighed: 12 dec. 201615 dec. 2016
Konferencens nummer: 6
http://www.ipta-conference.com/ipta16/

Konference

KonferenceIEEE International Conference on Image Processing Theory, Tools and Applications
Nummer6
LandFinland
ByOulu
Periode12/12/201615/12/2016
Internetadresse

Fingerprint

Interpolation
Image resolution
Fusion reactions
Cameras

Citer dette

Rasti, P., Orlova, O., Tamberg, G., Ozcinar, C., Nasrollahi, K., Moeslund, T. B., & Anbarjafari, G. (2016). Improved Interpolation Kernels for Super-resolution Algorithms. I IEEE International Conference on Image Processing Theory, Tools and Applications IEEE. https://doi.org/10.1109/IPTA.2016.7820980
Rasti, Pejman ; Orlova, Olga ; Tamberg, Gert ; Ozcinar, Cagri ; Nasrollahi, Kamal ; Moeslund, Thomas B. ; Anbarjafari, Gholamreza. / Improved Interpolation Kernels for Super-resolution Algorithms. IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, 2016.
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title = "Improved Interpolation Kernels for Super-resolution Algorithms",
abstract = "Super resolution (SR) algorithms are widely used in forensics investigations to enhance the resolution of images captured by surveillance cameras. Such algorithms usually use a common interpolation algorithm to generate an initial guess for the desired high resolution (HR) image. This initial guess is usually tuned through different methods, like learning-based or fusion-based methods, to converge the initial guess towards the desired HR output. In this work, it is shown that SR algorithms can result in better performance if more sophisticated kernels than the simple conventional ones are used for producing the initial guess. The contribution of this work is to introduce such a set of kernels which can be used in the context of SR. The quantitative and qualitative results on many natural, facial and iris images show the superiority of the generated HR images over two state-of-the-art SR algorithms when their original interpolation kernel is replaced by the ones introduced in this work.",
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Rasti, P, Orlova, O, Tamberg, G, Ozcinar, C, Nasrollahi, K, Moeslund, TB & Anbarjafari, G 2016, Improved Interpolation Kernels for Super-resolution Algorithms. i IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, IEEE International Conference on Image Processing Theory, Tools and Applications, Oulu, Finland, 12/12/2016. https://doi.org/10.1109/IPTA.2016.7820980

Improved Interpolation Kernels for Super-resolution Algorithms. / Rasti, Pejman; Orlova, Olga; Tamberg, Gert; Ozcinar, Cagri; Nasrollahi, Kamal; Moeslund, Thomas B.; Anbarjafari, Gholamreza.

IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, 2016.

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

TY - GEN

T1 - Improved Interpolation Kernels for Super-resolution Algorithms

AU - Rasti, Pejman

AU - Orlova, Olga

AU - Tamberg, Gert

AU - Ozcinar, Cagri

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

AU - Anbarjafari, Gholamreza

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AB - Super resolution (SR) algorithms are widely used in forensics investigations to enhance the resolution of images captured by surveillance cameras. Such algorithms usually use a common interpolation algorithm to generate an initial guess for the desired high resolution (HR) image. This initial guess is usually tuned through different methods, like learning-based or fusion-based methods, to converge the initial guess towards the desired HR output. In this work, it is shown that SR algorithms can result in better performance if more sophisticated kernels than the simple conventional ones are used for producing the initial guess. The contribution of this work is to introduce such a set of kernels which can be used in the context of SR. The quantitative and qualitative results on many natural, facial and iris images show the superiority of the generated HR images over two state-of-the-art SR algorithms when their original interpolation kernel is replaced by the ones introduced in this work.

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Rasti P, Orlova O, Tamberg G, Ozcinar C, Nasrollahi K, Moeslund TB et al. Improved Interpolation Kernels for Super-resolution Algorithms. I IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE. 2016 https://doi.org/10.1109/IPTA.2016.7820980