Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Polarization-Based Illumination Detection for Coherent Augmented Reality Scene Rendering in Dynamic Environments Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Synthesis-by-Analysis Network with Applications in Image Super-Resolution

Authors : Lechao Cheng, Zhangye Wang

Published in: Advances in Computer Graphics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recent studies have demonstrated the successful application of convolutional neural networks in single image super-resolution. In this paper, we present a general synthesis-by-analysis network for super-resolving a low-resolution image. Unlike Laplacian Pyramid Super-Resolution Network (LapSRN) that progressively reconstructs the sub-band residuals of high-resolution images, our proposed network breaks through the sequential dependency to expand the input and output into multiple disjoint bandpass signals. At each band, we perform the nonlinear mapping in truncated frequency interval by applying a carefully designed sub-network. Specifically, we propose a validated network sub-structure that considers both efficiency and accuracy. We also perform exhaustive experiments in existing commonly used dataset. The recovered high-resolution image is competitive or even superior in quality compared to those images produced by other methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

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

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

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

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

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




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

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




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Development and Usability Analysis of a Mixed Reality GPS Navigation Application for the Microsoft HoloLens
next chapter Towards Moving Virtual Arms Using Brain-Computer Interface
Literature
1.
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)
2.
Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, p. I. IEEE (2004)
3.
Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
4.
5.
6.
Huang, H., He, R., Sun, Z., Tan, T., et al.: Wavelet-SRNet: a wavelet-based CNN for multi-scale face super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1689–1697 (2017)
7.
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)
8.
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
9.
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate super resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, p. 5 (2017)
10.
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017
11.
Salvador, J., Perez-Pellitero, E.: Naive Bayes super-resolution forest. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 325–333 (2015)
12.
Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3791–3799 (2015)
13.
14.
Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
15.
Timofte, R., et al.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1110–1121. IEEE (2017)
16.
Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 370–378 (2015)
17.
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)MathSciNetCrossRef
18.
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. arXiv preprint arXiv:​1807.​02758 (2018)
19.
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
20.
Zou, W.W., Yuen, P.C.: Very low resolution face recognition problem. IEEE Trans. Image Process. 21(1), 327–340 (2012)MathSciNetCrossRef
Metadata
Title
A Synthesis-by-Analysis Network with Applications in Image Super-Resolution
Authors
Lechao Cheng
Zhangye Wang
Copyright Year
2019
Book
Advances in Computer Graphics

Print ISBN: 978-3-030-22513-1

Electronic ISBN: 978-3-030-22514-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-22514-8_42

Premium Partner

    Image Credits