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:
A Probabilistic Rating System for Team Competitions with Individual Contributions Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Color Image Restoration with Fuzzy Gaussian Mixture Model Driven Nonlocal Filter

Authors : V. B. Surya Prasath, Radhakrishnan Delhibabu

Published in: Analysis of Images, Social Networks and Texts

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Color image denoising is one of the classical image processing problem and various techniques have been explored over the years. Recently, nonlocal means (NLM) filter is proven to obtain good results for denoising Gaussian noise corrupted digital images using weighted mean among similar patches. In this paper, we consider fuzzy Gaussian mixture model (GMM) based NLM method for removing mixed Gaussian and impulse noise. By computing an automatic homogeneity map we identify impulse noise locations and utilize an adaptive patch size. Experimental results on mixed noise affected color images show that our scheme performs better than NLM, anisotropic diffusion and GMM-NLM over different noise levels. Comparison with respect to structural similarity, color image difference, and peak signal to noise ratio error metrics are undertaken and our scheme performs well overall without generating color artifacts.

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 Shape Matching Based on Skeletonization and Alignment of Primitive Chains
next chapter A Phase Unwrapping Algorithm for Interferometric Phase Images
Footnotes
1
Color image converted using MATLAB’s rgb2gray which uses the following formula \(I_g = 0.2989 * R + 0.5870 * G + 0.1140 * B\), where R, G, B are the red, green, and blue channels of the given color image. Images are normalized to [0, 1].
 
2
For a non-diagonal symmetric matrix \(\varSigma \) there exists orthogonal matrix \(\mathcal {O}\) and diagonal matrix \(\varLambda \) such that \(\varSigma ^{-1} = \mathcal {O}\varLambda \mathcal {O}\).
 
Literature
1.
Prasath, V.B.S., Singh, A.: A hybrid convex variational model for image restoration. Appl. Math. Comput. 215(10), 3655–3664 (2010)MathSciNetCrossRefMATH
2.
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRef
3.
Prasath, V.B.S., Vorotnikov, D.: On a system of adaptive coupled PDEs for image restoration. J. Math. Imaging Vis. 48(1), 35–52 (2014)MathSciNetCrossRefMATH
4.
Prasath, V.B.S., Delhibabu, R.: Automatic contrast parameter estimation in anisotropic diffusion for image restoration. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorsky, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 198–206. Springer, Heidelberg (2014)
5.
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision, pp. 59–66 (1998)
6.
Buades, A., Coll, B., Morel, J.M.: A review of image denoising methods, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetCrossRefMATH
7.
Prasath, V.B.S., Moreno, J.C.: Feature preserving anisotropic diffusion for image restoration. In: Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2013), India, pp. 1–4, December 2013
8.
Luszczkiewicz-Piatek, M.: Gaussian mixture model based non-local means technique for mixed noise suppression in color images. In: Choraś, R.S. (ed.) Image Processing & Communications Challenges. Advances in Intelligent Systems and Computing, vol. 313, pp. 75–83. Springer, Switzerland (2015)
9.
Zeng, J., Liu, Z.Q.: Type-2 Fuzzy Graphical Models for Pattern Recognition. Studies in Computational Intelligence, vol. 666. Springer, Berlin (2015)
10.
Nabney, I.: NETLAB: Algorithms for Pattern Recognitions. Springer, London (2002)MATH
11.
Prasath, V.B.S., Singh, A.: Multispectral image denoising by well-posed anisotropic diffusion scheme with channel coupling. Int. J. Remote Sens. 31(8), 2091–2099 (2010)CrossRef
Metadata
Title
Color Image Restoration with Fuzzy Gaussian Mixture Model Driven Nonlocal Filter
Authors
V. B. Surya Prasath
Radhakrishnan Delhibabu
Copyright Year
2015
Book
Analysis of Images, Social Networks and Texts

Print ISBN: 978-3-319-26122-5

Electronic ISBN: 978-3-319-26123-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26123-2_13

Premium Partner

    Image Credits