nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition

verfasst von : Yingyue Zhou, Hongbin Zang, Su Xu, Hongying Zhang

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary learning technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair learning is lower.

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Single Image Dehazing Based on Visual-Physical Model

Nächstes Kapitel Single Remote Sensing Image Haze Removal Based on Spatial and Spectral Self-Adaptive Model

Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)

Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

Dai, S., Han, M., Xu, W.: SoftCuts: a soft edge smoothness prior for color image super-resolution. IEEE Trans. Image Process. 18(5), 969–981 (2009)MathSciNetMATH

Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40(1), 25–47 (2000)MATH

Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-Based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56–65 (2002)

Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)

Yang, J., Wright, J., Huang, T., et al.: Image super-resolution as sparse representation of raw image patches. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

Wang, J., Zhu, S., Gong, Y.: Resolution enhancement based on learning the sparse association of image patches. Pattern Recogn. Lett. 31(1), 1–10 (2010)

Yang, J., Wright, J., Huang, T., et al.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 1–8 (2010)MathSciNetMATH

10.

Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces. Lecture Notes in Computer Science, vol. 6920, pp. 711–730. Springer, Heidelberg (2010)

11.

Rubinstein, R., Zibulevsky, M., Elad, M.: Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Technical report, Technion-Israel Institute of Technology (2008)

12.

Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)MATH

13.

Dong, W., Zhang, L., Lukac, R., Shi, G.: Nonlocal centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1382–1394 (2013)MathSciNetMATH

14.

Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(7), 3085–3098 (2014)MathSciNetMATH

15.

Fan, W., Yeung, D.Y.: Image hallucination using neighbor embedding over visual primitive manifolds. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (2007)

16.

Li, H., Xiong, H., Qian L.: Image super-resolution with sparse representation prior on primitives. In: Proceedings of Visual Communications and Image Processing (2010)

17.

Elad, M.: Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing. Springer, New York (2010)MATH

18.

Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNet

19.

Zhou, Y., Ye, Z., Xiao, Y.: A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise. J. Vis. Commun. Image Represent. 24(3), 283–294 (2013)

20.

Bao, G., Ye, Z., Xu, X., et al.: A compressed sensing approach to blind separation of speech mixture based on a two-layer sparsity mode. IEEE Trans. Audio Speech Lang. Process. 21(5), 899–906 (2013)

21.

Lin, C.H., Tsai, J.S., Chiu, C.T.: Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans. Image Process. 19(9), 2307–2320 (2010)MathSciNetMATH

22.

Tropp, J.A.: Greed is good: algorithmic results for sparse approximation. IEEE Trans. Inf. Theory 50, 2231–2242 (2004)MathSciNetMATH

23.

Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

Titel: Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition
verfasst von: Yingyue Zhou
Hongbin Zang
Su Xu
Hongying Zhang
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-21968-4

Electronic ISBN: 978-3-319-21969-1

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-21969-1_32

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner