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Non-local Mean-Shift filter for the reduction of multiplicative noise in digital images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper a new method for the reduction of multiplicative noise in digital images is described. The proposed algorithm is a modification of the Mean-Shift (MS) filter which is based on the concept of the Non-Local Means (NLM) denoising. The proposed algorithm does not focus on single pixels only, as in the case of the mean-shift technique, but also on their neighborhoods. The performance of the novel approach is experimentally verified and the obtained results prove that the new design is superior both to the MS and NLM techniques.
Rocznik
Tom
Strony
103--110
Opis fizyczny
Bibliogr. 11 poz., rys., tab., wykr.
Twórcy
autor
  • Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, Akademicka 16, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • [1] BUADES A, COLL B., MOREL J. M., A non-local algorithm for image denoising, In Computer Vision and Pattern Recognition, 2005, CVPR 2005, IEEE Computer Society Conference on, 2005, Vol. 2, pp. 60–65.
  • [2] BUADES A., COLL B., MOREL J.-M., A review of image denoising algorithms, with a new one, Multiscale Modeling and Simulation, 2005, Vol. 4 (2), pp. 490–530.
  • [3] BUADES A., COLL B., MOREL J.-M., Non-Local Means Denoising, Image Processing On Line, 2011, Vol. 1.
  • [4] CHENG Y., Mean shift, mode seeking, and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, Vol. 17 (8), pp. 790–799.
  • [5] COMANICIU D., MEER P., Mean shift analysis and applications, In Computer Vision, 1999, The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, Vol. 2, pp. 1197–1203.
  • [6] COMANICIU D., MEER P., Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, Vol. 24 (5), pp. 603–619.
  • [7] FUKUNAGA K., HOSTETLER L., The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, 1975, Vol. 21 (1), pp. 32–40.
  • [8] JUNEZ-FERREIRA C. A., VELASCO-AVALOS F. A., A simple algorithm for image denoising based on non-local means and preliminary segmentation, In Electronics, Robotics and Automotive Mechanics Conference, 2009, CERMA ’09, 2009, pp. 204–208.
  • [9] KARNATI V., ULIYAR M., DEY S., Fast non-local algorithm for image denoising, In 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 3873–3876.
  • [10] YU F., LAI X., A study of a denoising method for three dimensional data based on mean shift, In Intelligent Computation and Bio-Medical Instrumentation (ICBMI), 2011 International Conference on, pp. 39–42.
  • [11] ZHANG Y. J., Advances in image and video segmentation, IGI Global, 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a19770e0-b561-4fba-afed-6c89abdbc990
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