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Video resolution enhancement using deep neural networks and intensity based registrations

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posted on 2019-06-10, 12:51 authored by Gholamreza Anbarjafari
© 2018 ICIC International. Thanks to the recent rapid improvements made to the maximum possible resolution of display devices, higher qualities of experience have been made possible, which necessitates either producing and transmitting considerably higher volumes of data or super-resolving lower-resolution contents at the display side, where the former might not be practically feasible. Therefore, aiming at the latter, this paper proposes a novel super-resolution technique, which takes advantage of convolutional neural networks. Each image is registered into a window consisting of two frames, the second one standing for the reference image, using various intensity-based techniques, which have been tested and compared throughout the paper. According to the experimental results, the proposed method leads to substantial enhancements in the quality of the super-resolved images, compared with the state-of-the-art techniques introduced within the existing literature. On the Akiyo video sequence, on average, the result possesses 5.38dB higher PSNR values than those of the Vandewalle registration technique, with structure adaptive normalised convolution being utilized as the reconstruction approach.

Funding

This work has been partially supported by the Estonian Research Council Grant (PUT638) and the Scientific and Technological Research Council of Turkey (TUBTAK) (Project 1001-116E097).

History

School

  • Loughborough University London

Published in

International Journal of Innovative Computing, Information and Control

Volume

14

Issue

5

Pages

1969 - 1976

Citation

ANBARJAFARI, G., 2018. Video resolution enhancement using deep neural networks and intensity based registrations. International Journal of Innovative Computing, Information and Control, 14(5), pp. 1969 - 1976.

Publisher

© ICIC International

Version

  • VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2018-10-01

Notes

This paper was published in the journal International Journal of Innovative Computing, Information and Control and the definitive published version is available at https://doi.org/10.24507/ijicic.14.05.1969

ISSN

1349-4198

Language

  • en

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