Advertisement

Application of Local Binary Pattern to Windowed Nonlocal Means Image Denoising

  • Fakhry Khellah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

This paper presents a new technique to image denoising that mainly addresses the incurred high blurring when the windowed nonlocal means is applied to images corrupted by high noise levels. The proposed method is based on an enhanced weighting function that computes patches similarity based on both their intensities and structural features. The structural features are encoded using Local Binary Pattern (LBP) a well known texture descriptors. A new LBP based weighting function is proposed that has properties complementing the intensity based weighting function. The LBP based weighting function is used to modulate the intensity based weighting function. The modulated weights are noise independent and reflect the actual patch similarity. The method is found to be quantitatively and qualitatively effective in denoising images when corrupted by high noise levels. It suppresses image noise while preserving significant image characteristics.

Keywords

Local Binary Patterns Nonlocal means Image Denoising 

References

  1. 1.
    Brox, T., Kleinschmidt, O., Cremers, D.: Efficient nonlocal means for denoising of textural patterns. IEEE Trans. Image Process. 17(7), 1083–1092 (2008)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: International Conference on Computer Vision and Pattern Recognition, pp. 60–65 (2005)Google Scholar
  3. 3.
    Kervrann, C., Boulanger, J.: Optimal spatial adaptation for patch-based image denoising. IEEE Trans. Image Process. 15, 2866–2878 (2006)CrossRefGoogle Scholar
  4. 4.
    Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18, 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Tasdizen, T.: Principal neighborhood dictionaries for nonlocal image denoising. IEEE Trans. Image Process. 18(12), 2649–2660 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fakhry Khellah
    • 1
  1. 1.Prince Sultan UniversityRiyadhSaudi Arabia

Personalised recommendations