Abstract
In this paper a new approach to reduction of a mixture of Gaussian and random impulse noises in color images is presented. The proposed filtering scheme is based on the application of the Bilateral Filter in order to address the problem of impulse noise reduction by determining the rate of region homogeneity, for calculating the weights needed for the Non-Local Means (NLM) averaging operation. Gaussian Mixture Model approach is applied for determining similarity between local image regions. The proposed solution is capable to successfully suppress the mixed noise of various intensities, at a lower computational cost than NLM method, due to the adaptive choice of size of search window for similar local neighborhoods. Experimental results prove that the introduced design yields better results than the Non-Local Means and Anisotropic Diffusion techniques in the case of color images contaminated by strong mixed Gaussian and impulsive noise.
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References
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Conf. on Computer Vision and Pattern Recognition, USA, vol. 2, pp. 60–65 (2005)
Garnett, R., Huegerich, T., Chui, C., Wenjie, H.: A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing 14(11), 1747–1754 (2005)
Smolka, B.: Peer group switching fillter for impulse noise reduction in color images. Pattern Recognition Letters 31(6), 484–495 (2010)
Nikolova, M.: A variational approach to remove outliers and impulse noise. J. Math. Imag. Vis. 20(1), 99–120 (2004)
Tang, K., Astola, J., Neuvo, Y.: Nonlinear multivariate image filtering techniques. IEEE Transactions on Image Processing 4(6), 788–798 (1995)
Lukac, R., Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector sigma lters for noise detection and removal in color images. Journal of Visual Communication and Image Representation 17(1), 1–26 (2006)
Dong, Y., Chan, R., Xu, S.: A detection statistic for random-valued impulse noise. IEEE Trans. Image Process. 16(4), 1112–1120 (2007)
Lamichhane, B.P.: Finite Element Techniques for Removing the Mixture of Gaussian and Impulsive Noise. IEEE Transactions on Signal Processing 57(7), 2538–2547 (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846 (1998)
Luszczkiewicz, M., Smolka, B.: Application of bilateral filtering and Gaussian mixture modeling for the retrieval of paintings. In: Proc. of ICIP, pp. 77–80 (2009)
Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Research and Application 30(1), 21–30 (2005)
Łuszczkiewicz-Piątek, M.: Which Color Space Should Be Chosen for Robust Color Image Retrieval Based on Mixture Modeling. In: Choras, R.S. (ed.) Image Processing and Communications Challenges 5. AISC, vol. 233, pp. 55–64. Springer, Heidelberg (2014)
Bilmes, J.: A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. University of Berkeley, ICSI-TR-97-021 (1997)
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
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Luszczkiewicz-Piatek, M. (2015). Gaussian Mixture Model Based Non-Local Means Technique for Mixed Noise Suppression in Color Images. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_10
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DOI: https://doi.org/10.1007/978-3-319-10662-5_10
Publisher Name: Springer, Cham
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