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:
Cover of the book

2018 | OriginalPaper | Chapter

A New Accurate Image Denoising Method Based on Sparse Coding Coefficients

Authors : Kai Lin, Ge Li, Yiwei Zhang, Jiaxing Zhong

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Although sparse coding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparse coding noise is not tight enough. To suppress the sparse coding noise, we exploit a couple of images to estimate unknown sparse code. There are two main contributions in this paper: The first is to use a reference denoised image and an intermediate denoised image to estimate the sparse coding coefficients of the original image. The second is that we set a threshold to rule out blocks of low similarity to improve the accuracy of estimation. Our experimental results have shown improvements over several state-of-the-art denoising methods on a collection of 12 generic natural images.

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
next chapter A Novel Frontal Facial Synthesis Algorithm Based on Individual Residual Face
Literature
1.
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)CrossRef
2.
Simoncelli, E.P., Adelson, E.H.: Noise removal via Bayesian wavelet coring. In: Proceedings of International Conference on Image Processing, vol. 1, pp. 379–382 (1996)
3.
Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process. A Publ. IEEE Sig. Process. Soc. 12(11), 1338–1351 (2003)MathSciNetCrossRefMATH
4.
Donoho, D.L.: De-noising by soft-thresholding. IEEE Press (1995)
5.
6.
Chen, F., Zhang, L., Yu, H.: External patch prior guided internal clustering for image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 603–611 (2015)
7.
Shao, L., Zhang, H., De Haan, G.: An overview and performance evaluation of classification-based least squares trained filters. IEEE Trans. Image Process. 17(10), 1772–1782 (2008)MathSciNetCrossRefMATH
8.
Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)MathSciNetCrossRef
9.
Xiong, R., Liu, H., Zhang, X., Zhang, J., Ma, S., Wu, F., Gao, W.: Image denoising via bandwise adaptive modeling and regularization exploiting nonlocal similarity. IEEE Trans. Image Process. 25(12), 5793–5805 (2016)MathSciNetCrossRef
10.
Ma, S., Zhang, X., Zhang, J., Jia, C., Wang, S., Gao, W.: Nonlocal in-loop filter: the way toward next-generation video coding? IEEE Multimedia 23(2), 16–26 (2016)CrossRef
11.
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65 IEEE (2005)
12.
Goossens, B., Luong, H., Piurica, A., Philips, W.: An improved non-local denoising algorithm. In: 2008 International Workshop on Local and Non-local Approximation in Image Processing (LNLA 2008), pp. 143–156 (2008)
13.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRef
14.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2272–2279. IEEE (2009)
15.
Dong, W., Li, X., Zhang, L., Shi, G.: Sparsity-based image denoising via dictionary learning and structural clustering. In: Computer Vision and Pattern Recognition, pp. 457–464 (2011)
16.
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)MathSciNetCrossRefMATH
17.
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)MathSciNetCrossRefMATH
18.
Bhujle, H.: Feature-preserving 3D fluorescence image sequence denoising. In: Tenth Indian Conference on Computer Vision, Graphics and Image Processing, p. 45 (2016)
19.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
20.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: BM3D image denoising with shape-adaptive principal component analysis. In: Proceedings of Workshop on Signal Processing with Adaptive Sparse Structured Representation, Saint-Malo (2009)
Metadata
Title
A New Accurate Image Denoising Method Based on Sparse Coding Coefficients
Authors
Kai Lin
Ge Li
Yiwei Zhang
Jiaxing Zhong
Copyright Year
2018
Book
MultiMedia Modeling

Print ISBN: 978-3-319-73599-3

Electronic ISBN: 978-3-319-73600-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73600-6_1