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HeNLM-LA: a locally adaptive non-local means algorithm based on hermite functions expansion

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Abstract

A new noise reduction algorithm HeNLM-LA is proposed. It is a modification of the non-local means algorithm using Hermite functions expansion of pixel neighborhoods. The filtering strength parameter is automatically adjusted proportionally to the local noise level. An algorithm for local noise level estimation is based on edge modeling; it suppresses high-amplitude edges in the map of local image variance.

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References

  1. Buades, A., A non-local algorithm for image denoising, IEEE Conference on Computer Vision and Pattern Recognition, 2005, vol. 2, pp. 60–65.

    Google Scholar 

  2. Manzanera, A., Local Jet based similarity for NL-Means filtering, 20th International Conference on Computer Vision and Pattern Recognition (ICPR), 2010, pp. 2668–2671.

    Google Scholar 

  3. Wang, S., Xia, Y., Liu, Q., Luo, J., Zhu, Y., and Feng, D., Gabor feature based nonlocal means filter for textured image denoising, Journal of Visual Communication and Image Representation, Oct. 2012, vol. 23,issue 7, pp. 1008–1018.

    Article  Google Scholar 

  4. Yaroslavsky, L.P. and Kim, V., Rank algorithms for picture processing, computer vision, Graphics and Image Processing, 1986, vol. 35, pp. 234–258.

    Article  Google Scholar 

  5. Tomasi, C. and Manduchi, R., Bilateral filtering for gray and color images, Sixth International Conference on Computer Vision (ICCV98), 1998, pp. 839–846

    Google Scholar 

  6. Forstner, W., Image preprocessing for feature extraction in digital intensity, color and range images, Springer Lecture Notes on Earth Sciences, 2000, vol. 95, pp. 165–189.

    Article  Google Scholar 

  7. Ce Liu, Szeliski, R., Sing Bing Kang, Zitnick, C.L., and Freeman, W.T., Automatic estimation and removal of noise from a single image, IEEE Transactions on Pattern Analysis and Machine Intelligence, Feb. 2008, vol. 30, no. 2, pp. 299–314.

    Article  Google Scholar 

  8. Pyatykh, St., Hesser, J., and Lei Zheng, Image noise level estimation by principal component analysis, IEEE Transactions on Image Processing, 2013, vol. 22, no. 2, pp. 687–699.

    Article  MathSciNet  Google Scholar 

  9. Storozhilova, M.V., Lukin, A.S., Yurin, D.V., and Sinitsyn, V.E., 2.5D extension of neighborhood filters for noise reduction in 3D medical CT images, Lecture Notes in Computer Science, 2013, vol. 7870, pp. 1–16.

    Article  Google Scholar 

  10. Lindeberg, T., Scale-Space Theory in Computer Vision, Dordrecht: Kluwer Academic Publishers, 1994.

    Book  Google Scholar 

  11. Abramowitz, M. and Stegun, I.A., Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, U.S. National Bureau of Standards, 1964; Dover, New York 1965.

    MATH  Google Scholar 

  12. Krylov, A.S., Kutovoi, A.V., and Wee Kheng Leow, Texture parameterization with Hermite functions, Proc. of the 12th International Conference Graphicon’2002, Russia, Nizhny Novgorod, 2002, pp. 190–194.

    Google Scholar 

  13. Canny, J., A computational approach to edge detection, IEEE PAMI, 1986, vol. 8, pp. 34–43.

    Google Scholar 

  14. Lindeberg, T., Edge detection and ridge detection with automatic scale selection, International Journal of Computer Vision, 1998, vol. 30, no. 2, pp. 117–154.

    Article  Google Scholar 

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Correspondence to N. V. Mamaev.

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Original Russian Text © N.V. Mamaev, A.S. Lukin, D.V. Yurin, 2014, published in Programmirovanie, 2014, Vol. 40, No. 4.

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Mamaev, N.V., Lukin, A.S. & Yurin, D.V. HeNLM-LA: a locally adaptive non-local means algorithm based on hermite functions expansion. Program Comput Soft 40, 199–207 (2014). https://doi.org/10.1134/S0361768814040070

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  • DOI: https://doi.org/10.1134/S0361768814040070

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