Bounded Non-Local Means for Fast and Effective Image Denoising

  • Federico TombariEmail author
  • Luigi Di Stefano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Non-Local Means (NLM) is a powerful but computationally expensive image denoising algorithm, which estimates a noiseless pixel as a weighted average across a large surrounding region whereby pixels centered at more similar patches are given higher weights. In this paper, we propose a method aimed at improving the computational efficiency of NLM by quick pre-selection of dissimilar patches thanks to a rapidly computable upper bound of the weighting function. Unlike previous approaches, our technique mathematically guarantees all highly correlated patches to be accounted for while discarding dissimilar ones, this providing not only faster speed but improved denoising too.


Non local means Image denoising Fast bounding method 


  1. 1.
    Brox, T., Kleinschmid, O., Cremers, D.: Efficient nonlocal means for denoising of textural patterns. IEEE Trans. Image Processing 17(7), 1083–1092 (2008)CrossRefGoogle Scholar
  2. 2.
    Buades, A., Coll, B., Morel, J.: A review of image denoising methods, with a new one. SIAM Multiscale Modeling and Simulation 4(2), 490–530 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Crow, F.: Summed-area tables for texture mapping. Computer Graphics 18(3), 207–212 (1984)CrossRefGoogle Scholar
  4. 4.
    Danielyan, A., Katkovnik, V., Egiazarian, K.: Bm3d frames and variational image deblurring. IEEE Trans. Image Processing 21(4), 1715–1728 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Karnati, V., Uliyar, M., Dey, S.: Fast non-local algorithm for image denoising. In: Proc. Int. Conf. on Image Processing (ICIP) (2009)Google Scholar
  6. 6.
    Lewis, J.: Fast template matching. Vision Interface, pp. 120–123 (1995)Google Scholar
  7. 7.
    Liu, Y., Wang, J., Chen, X., Guo, Y., Peng, Q.: A robust and fast non-local means algorithm for image denoising. J. Computer Science and Technology 23(2), 270–279 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Processing Letters 12(12), 839–842 (2005)CrossRefGoogle Scholar
  9. 9.
    McDonnell, M.: Box-filtering techniques. Computer Graphics and Image Processing 17(1), 65–70 (1981)CrossRefGoogle Scholar
  10. 10.
    Ouyang, W., Tombari, F., Mattoccia, S., Di Stefano, L., Cham, W.K.: Performance evaluation of full search equivalent pattern matching algorithms. Trans. Pattern Analysis and Machine Intelligence (PAMI) 34(1), 127–143 (2012)CrossRefGoogle Scholar
  11. 11.
    Tasdizen, T.: Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Trans. Image Processing 18(12), 2649–2660 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tombari, F., Mattoccia, S., Di Stefano, L.: Full search-equivalent pattern matching with incremental dissimilarity approximations. Trans. Pattern Analysis and Machine Intelligence (PAMI) 31(1), 129–141 (2009)CrossRefGoogle Scholar
  13. 13.
    Vignesh, R., Oh, B., Kuo, C.: Fast non-local means (nlm) computation with probabilistic early termination. IEEE Signal Processing Letters 17(3), 277–280 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of BolognaBolognaItaly

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