HeNLM-LA3D: A three-dimensional locally adaptive Hermite functions expansion based non-local means algorithm for CT applications
- 42 Downloads
A three-dimensional filtering algorithm for CT images (HeNLM-LA3D) has been proposed that is based on expanding the pixel neighborhood into Hermite functions, which form an orthonormal basis. Accounting for Hermite functions properties, pixel neighborhoods are oriented according to principal components of the structure tensor. The filtering parameter is adaptively adjusted to local estimates of the noise level. A noise estimation algorithm is proposed.
Keywordscomputed tomography image filtering non-local means local jets method Hermite functions 3D filtering locally adaptive algorithm
Unable to display preview. Download preview PDF.
- 1.M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (U.S. National Bureau of Standards, 1964; Dover, New York, 1965).Google Scholar
- 2.A. Buades, “A non-local algorithm for image denoising,” in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (Madison, WI, 2005), Vol. 2, pp. 60–65.Google Scholar
- 3.J. Canny, “A computational approach to edge detection,” IEEE PAMI 8, 34–43 (1986).Google Scholar
- 7.H. Knutsson, “Representing local structure using tensors,”in Proc. 6th Scandinavian Conf. on Image Analysis (Oulu Univ., Oulu, 1989), pp. 244–251.Google Scholar
- 8.A. S. Krylov, A. V. Kutovoi, and Wee Kheng Leow, “Texture parameterization with hermite functions,” in Proc. 12th Int. Conf. Graphicon’2002 (Nizhny Novgorod, 2002), pp. 190–194.Google Scholar
- 10.N. Mamaev, A. Lukin, D. Yurin, M. Glazkova, and V. Sinitsin, “Hermite functions expansion based nonlocal means algorithm for CT applications,” in Proc. of 11th Int. Conference “Pattern Recognition and Image Analysis: New Information Technologies” (Samara, 2013), Vol. 2, pp. 638–641.Google Scholar
- 11.A. Manzanera, “Local jet based similarity for NLmeans filtering,” in Proc. 20th Int. Conf. on Computer Vision and Pattern Recognition (ICPR) (Istanbul, 2010), pp. 2668–2671.Google Scholar
- 13.T. H. Newton and D. G. Potts, Radiology of the Skull and Brain, Vol. 5: Technical Aspects of Computed Tomography (C. V. Mosby Co., St. Louis, 1981), p. 585.Google Scholar
- 16.C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. 6th Int. Conf. on Computer Vision (ICCV’98) (Bombay, 1998), pp. 839–846.Google Scholar