Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

22-02-2020 | Original Article | Issue 8/2020

International Journal of Machine Learning and Cybernetics 8/2020

Deep quantification down-plain-upsampling residual learning for single image super-resolution

Journal:
International Journal of Machine Learning and Cybernetics > Issue 8/2020
Authors:
Shuying Huang, Haijun Zhu, Yong Yang, Yifan Zuo, Yingjun Tang
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Deep convolutional neural networks have been widely used in single image super-resolution (SISR) with great success. However, the performance and efficiency of such models need to be improved for practical applications. In this paper, a novel deep quantification down-plain-upsampling (QDPU) network for SISR is proposed. In the framework, a down-plain-upsampling (DPU) residual block based on U-Net is firstly designed to reduce the computational cost by transforming the spatial scale of feature maps without sacrificing the reconstruction performance. Then, to better transmit low-level features to the reconstruction layer, we construct quantification skip-connection modules to integrate the outputs of the DPU residual blocks. Finally, QDPU is developed by stacking the DPU residual blocks with multiple skip-connections to reconstruct high-resolution images and reduce the computational burden. Quantitative and qualitative evaluations of the reconstruction results on four benchmark datasets show that the proposed method can achieve better performance compared with several state-of-the-art SISR methods.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 8/2020

International Journal of Machine Learning and Cybernetics 8/2020 Go to the issue