Skip to main content

2020 | OriginalPaper | Buchkapitel

Single-Image Super-Resolution: A Survey

verfasst von : Tingting Yao, Yu Luo, Yantong Chen, Dongqiao Yang, Lei Zhao

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Single-image super-resolution has been broadly applied in many fields such as military term, medical imaging, etc. In this paper, we mainly focus on the researches of recent years and classify them into non-deep learning SR algorithms and deep learning SR algorithms. For each classification, the basic concepts and algorithm processes are introduced. Furthermore, the paper discusses the advantages and disadvantages of different algorithms, which will offer potential research direction for the future development of SR.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

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!

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!

Literatur
1.
Zurück zum Zitat Ahmed J, Shah MA. Single image super-resolution by directionally structured coupled dictionary learning. Eurasip J Image Video Process. 2016;1:36. Ahmed J, Shah MA. Single image super-resolution by directionally structured coupled dictionary learning. Eurasip J Image Video Process. 2016;1:36.
2.
Zurück zum Zitat Ahmed J, Klette R. Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution. In: International conference on pattern recognition. IEEE; 2017. Ahmed J, Klette R. Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution. In: International conference on pattern recognition. IEEE; 2017.
3.
Zurück zum Zitat Chang H, Yeung DY, Xiong Y. Super-resolution through neighbor embedding. Proc Comput Vis Pattern Recogn. 2004;1:I-275–82. Chang H, Yeung DY, Xiong Y. Super-resolution through neighbor embedding.  Proc Comput Vis Pattern Recogn. 2004;1:I-275–82.
4.
Zurück zum Zitat Dong C, et al. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295–307.CrossRef Dong C, et al. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295–307.CrossRef
5.
Zurück zum Zitat Freeman WT, Jones TR, Pasztor EC. Example-based super-resolution. Comput Graph Appl. 2002;2:56–65 (IEEE22.).CrossRef Freeman WT, Jones TR, Pasztor EC. Example-based super-resolution. Comput Graph Appl. 2002;2:56–65 (IEEE22.).CrossRef
6.
Zurück zum Zitat Gu S, et al. Convolutional sparse coding for image super-resolution. In: IEEE international conference on computer vision. IEEE; 2015. p. 1823–31. Gu S, et al. Convolutional sparse coding for image super-resolution. In: IEEE international conference on computer vision. IEEE; 2015. p. 1823–31.
7.
Zurück zum Zitat Huang JB, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars. Comput Vis Pattern Recogn. 2015:5197–206. Huang JB, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars. Comput Vis Pattern Recogn. 2015:5197–206.
8.
Zurück zum Zitat Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. Comput Vis Pattern Recogn. 2016:1646–54. Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. Comput Vis Pattern Recogn. 2016:1646–54.
9.
Zurück zum Zitat Kim J, Lee JK, Lee KM. Deeply-recursive convolutional network for image super-resolution. Comput Vis Pattern Recogn. 2016:1637–45. Kim J, Lee JK, Lee KM. Deeply-recursive convolutional network for image super-resolution. Comput Vis Pattern Recogn. 2016:1637–45.
10.
Zurück zum Zitat Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors. In: International conference on neural information processing systems Curran Associates Inc.; 2009. p. 1033–41. Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors. In: International conference on neural information processing systems Curran Associates Inc.; 2009. p. 1033–41.
11.
Zurück zum Zitat Li X, et al. Single image super-resolution via subspace projection and neighbor embedding. Neurocomputing. 2014;139:310–20.CrossRef Li X, et al. Single image super-resolution via subspace projection and neighbor embedding.  Neurocomputing. 2014;139:310–20.CrossRef
12.
Zurück zum Zitat Liang Y, et al. Incorporating image priors with deep convolutional neural networks for image super-resolution. Neurocomputing. 2016;194:340–7.CrossRef Liang Y, et al. Incorporating image priors with deep convolutional neural networks for image super-resolution. Neurocomputing. 2016;194:340–7.CrossRef
13.
Zurück zum Zitat Song S, et al. Joint sub-band based neighbor embedding for image super-resolution. In: IEEE international conference on acoustics, speech and signal processing. IEEE; 2016. p. 1661–5. Song S, et al. Joint sub-band based neighbor embedding for image super-resolution. In: IEEE international conference on acoustics, speech and signal processing. IEEE; 2016. p. 1661–5.
14.
Zurück zum Zitat Sun X, Xiao-Guang LI, Jia-Feng LI, et al. Review on deep learning based image super-resolution restoration algorithms. Acta Automatica Sin. 2017;43(5):697–709. Sun X, Xiao-Guang LI, Jia-Feng LI, et al. Review on deep learning based image super-resolution restoration algorithms. Acta Automatica Sin. 2017;43(5):697–709.
15.
Zurück zum Zitat Timofte R, Rothe R, Gool LV. Seven ways to improve example-based single image super resolution; 2015. p. 1865–73. Timofte R, Rothe R, Gool LV. Seven ways to improve example-based single image super resolution; 2015. p. 1865–73.
16.
Zurück zum Zitat Timofte R, Smet VD, Gool LV. A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian conference on computer vision. Springer, Cham; 2014. p. 111–26. Timofte R, Smet VD, Gool LV. A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian conference on computer vision. Springer, Cham; 2014. p. 111–26.
17.
Zurück zum Zitat Wang Z, Yang Y, Wang Z, et al. Learning super-resolution jointly from external and internal examples. IEEE Trans Image Process. (A Publication of the IEEE Signal Processing Society). 2015;24(11):4359.MathSciNetCrossRef Wang Z, Yang Y, Wang Z, et al. Learning super-resolution jointly from external and internal examples. IEEE Trans Image Process. (A Publication of the IEEE Signal Processing Society). 2015;24(11):4359.MathSciNetCrossRef
18.
Zurück zum Zitat Yang J, et al. Image super-resolution via sparse representation. IEEE Trans Image Proces. 2010;19(11):2861–73. Yang J, et al. Image super-resolution via sparse representation. IEEE Trans Image Proces. 2010;19(11):2861–73.
19.
Zurück zum Zitat Yang W, et al. Deep edge guided recurrent residual learning for image super-resolution. IEEE Trans Image Process (A Publication of the IEEE Signal Processing Society). 2016;26(12):5895–907.MathSciNetCrossRef Yang W, et al. Deep edge guided recurrent residual learning for image super-resolution. IEEE Trans Image Process (A Publication of the IEEE Signal Processing Society). 2016;26(12):5895–907.MathSciNetCrossRef
20.
Zurück zum Zitat Yang W, et al. Image super-resolution via nonlocal similarity and group structured sparse representation. Vis Commun Image Process. 2016:1–4. Yang W, et al. Image super-resolution via nonlocal similarity and group structured sparse representation. Vis Commun Image Process. 2016:1–4.
21.
Zurück zum Zitat Zhang Y, et al. Image super-resolution based on, structure-modulated sparse representation. IEEE Trans Image Process. 2015;24(9):2797–810.MathSciNetCrossRef Zhang Y, et al. Image super-resolution based on, structure-modulated sparse representation. IEEE Trans Image Process. 2015;24(9):2797–810.MathSciNetCrossRef
Metadaten
Titel
Single-Image Super-Resolution: A Survey
verfasst von
Tingting Yao
Yu Luo
Yantong Chen
Dongqiao Yang
Lei Zhao
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-6504-1_16

Neuer Inhalt