Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Journal of Scientific Computing
Erschienen in: Journal of Scientific Computing 1/2016

05.07.2015

Image Deblurring Via Total Variation Based Structured Sparse Model Selection

verfasst von: Liyan Ma, Tieyong Zeng

Erschienen in: Journal of Scientific Computing | Ausgabe 1/2016

Einloggen

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

search-config
loading …

Abstract

In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image restoration in recent years. However, the commonly used overcomplete dictionary is not well structured. This shortcoming makes the approximation be unstable and demand much computational time. To overcome this, the structured sparse model selection (SSMS) over a family of learned orthogonal bases was proposed recently. In this paper, We further analyze the properties of SSMS and propose a model for deblurring under Gaussian noise. Numerical experimental results show that the proposed algorithm achieves competitive performance. As a generalization, we give a modified model for deblurring under salt-and-pepper noise. The resulting algorithm also has a good performance.
Nächster Artikel A Mixed Finite Element Discretisation of Thin Plate Splines Based on Biorthogonal Systems

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 "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

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
Literatur
1.
Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)CrossRef
2.
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2, 183–202 (2009)MathSciNetCrossRefMATH
3.
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4, 490–530 (2005)MathSciNetCrossRefMATH
4.
Cai, J., Chan, R., Nikolova, M.: Two-phase approach for deblurring images corrupted by impulse plus gaussian noise. Inverse Probl. Imaging 2, 187–204 (2008)MathSciNetCrossRefMATH
5.
Cai, J., Chan, R., Nikolova, M.: Fast two-phase image deblurring under impulse noise. J. Math. Imaging Vis. 36, 46–53 (2010)MathSciNetCrossRef
6.
Cai, J., Huang, S., Ji, H., Shen, Z., Ye, G.: Data-driven tight frame construction and image denoising. Appl. Comput. Harmon. Anal. 37, 89–105 (2014)MathSciNetCrossRefMATH
7.
Cai, J., Osher, S., Shen, Z.: Split Bregman mehods and frame based image restoration. Multiscale Model. Simul. 8, 337–369 (2010)MathSciNetCrossRefMATH
8.
Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imag. Vis. 20, 89–97 (2004)MathSciNetCrossRef
9.
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120–145 (2004)MathSciNetCrossRefMATH
10.
Chatterjee, P., Milanfar, P.: Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process. 18, 1438–1451 (2009)MathSciNetCrossRef
11.
Cho, T.S., Joshi, N., Zitnick, C.L., Kang, S.B., Szeliski, R., Freeman, W.T.: A content-aware image prior. In: CVPR, pp. 169–176 (2010)
12.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)MathSciNetCrossRef
13.
Dong, Y., Hintermüler, M., Neri, M.: An efficient primal-dual method for L1-TV image restoration. SIAM J. Imaging Sci. 2, 1168–1189 (2009)MathSciNetCrossRefMATH
14.
Dong, Y., Zeng, T.: A convex variational model for restoring blurred images with multiplicative noise. SIAM J. Imaging Sci. 6, 1598–1625 (2013)MathSciNetCrossRefMATH
15.
Effros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1033–1038 (1999)
16.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)MathSciNetCrossRef
17.
Elad, M., Figueiredo, M., Ma, Y.: On the role of sparse and redundant representations in image processing. Proc. IEEE 98, 972–982 (2010)CrossRef
18.
Figueiredo, M., Bioucas-Dias, J., Nowak, R.: Majorization–minimization algorithms for wavelet-based image restoration. IEEE Trans. Image Process. 15, 2980–2991 (2007)MathSciNetCrossRef
19.
Figueiredo, M., Bioucas-Dias, J.: Restoration of Poissonian images using alternating direction optimization. IEEE Trans. Image Process. 19, 3133–3145 (2010)MathSciNetCrossRef
20.
Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7, 1005–1028 (2008)MathSciNetCrossRefMATH
21.
22.
23.
Haghighat, M., Zonouz, S., Abdel-Mottaleb, M.: Identification using encrypted biometrics. Comput. Anal. Images Patterns 2, 440–448 (2013)MathSciNetCrossRef
24.
Hu, Z., Huang, J., Yang, M.: Single image deblurring with adaptive dictionary learning. In: Proceedings of IEEE ICIP, pp. 1169–1172 (2010)
25.
Huang, Y., Ng, M., Wen, Y.: A new total variation method for multiplicative noise removal. SIAM J. Imaging Sci. 2, 20–40 (2009)MathSciNetCrossRefMATH
26.
Hwang, H., Haddad, R.: Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. 4, 499–502 (1995)CrossRef
27.
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: NIPS, pp. 1033–1041 (2009)
28.
Li, Y., Shen, L., Dai, D., Suter, B.: Framelet algorithms for de-blurring images corrupted by impulse plus Gaussian noise. IEEE Trans. Image Process. 20, 123–139 (2011)MathSciNet
29.
Liu, Q., Liang, D., Song, Y., Luo, J., Zhu, Y., Li, W.: Augmented Lagrangian-based sparse representation method with dictionary updating for image deblurring. SIAM J. Imaging Sci. 6, 1689C–1718 (2013)MathSciNetCrossRefMATH
30.
31.
Ma, L., Yu, J., Zeng, T.: Sparse representation prior and total variation-based image deblurring under impulse noise. SIAM J. Imaging Sci. 6, 2258–2284 (2013)MathSciNetCrossRefMATH
32.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: Proceedings of ICCV, pp. 2272–2279 (2009)
33.
Mairal, J., Sapiro, G., Elad, M.: Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Model. Simul. 7, 214–241 (2008)MathSciNetCrossRefMATH
34.
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. Pattern Anal. Mach. Intell. 18, 837–842 (1996)CrossRef
35.
Neelamani, R., Choi, H., Baraniuk, R.: ForWaRD: Fourier wavelet regularized deconvolution for ill-condituined systems. IEEE Trans. Signal Process. 52, 418–433 (2004)MathSciNetCrossRef
36.
Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRef
37.
38.
Wu, C., Zhang, J., Tai, X.-C.: Augmented Lagrangian method for total variation restoration with non-quadratic fidelity. Inverse Probl. Imaging 5, 237–261 (2011)MathSciNetCrossRefMATH
39.
Xiao, Y., Zeng, T., Yu, J., Ng, M.K.: Restoration of images corrupted by mixed Gaussian-impulse noise via \(l^1-l^0\) minimization. Pattern Recognit. 44, 1708–1720 (2011)CrossRefMATH
40.
Yang, J., Zhang, Y., Yin, W.: An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise. SIAM J. Sci. Comput. 31, 2842–2865 (2009)MathSciNetCrossRefMATH
41.
Yu, G., Sapiro, G., Mallat, S.: Image modeling and enhancement via structured sparse model selection. In: ICIP, pp. 1641–1644 (2010)
42.
Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21, 2481–2499 (2012)MathSciNetCrossRef
43.
Zeng, T., Li, X., Ng, M.: Alternating minimization method for total variation based wavelet shrinkage model. Commun. Comput. Phys. 8, 976–994 (2010)MathSciNet
44.
Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3, 253–C276 (2010)MathSciNetCrossRefMATH
45.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV, pp. 479–486 (2011)
Metadaten
Titel
Image Deblurring Via Total Variation Based Structured Sparse Model Selection
verfasst von
Liyan Ma
Tieyong Zeng
Publikationsdatum
05.07.2015
Verlag
Springer US
Erschienen in
Journal of Scientific Computing / Ausgabe 1/2016
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI
https://doi.org/10.1007/s10915-015-0067-7

Weitere Artikel der Ausgabe 1/2016

Journal of Scientific Computing 1/2016 Zur Ausgabe

OriginalPaper

Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means

OriginalPaper

Preasymptotic Error Analysis of High Order Interior Penalty Discontinuous Galerkin Methods for the Helmholtz Equation with High Wave Number

OriginalPaper

A Simplified Formulation of the Flux Reconstruction Method

OriginalPaper

An Adaptive Rational Block Lanczos-Type Algorithm for Model Reduction of Large Scale Dynamical Systems

OriginalPaper

Pointwise Error Estimates and Two-Grid Algorithms of Discontinuous Galerkin Method for Strongly Nonlinear Elliptic Problems

OriginalPaper

An Implementable Splitting Algorithm for the $$\ell _1$$ ℓ 1 -norm Regularized Split Feasibility Problem