Open Access Journal

ISSN : 2394-6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Contrast Enhancement based on Gaussian Mixture Modeling with Noise Adaptive Fuzzy Switching Median Filter

Author : Jayasilpa S 1 Kavitha N Nair 2

Date of Publication :7th January 2015

Abstract: The proposed algorithm automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution. In a mixture distribution, its density function is just a convex combination (a linear combination in which all coefficients or weights sum to one) of other probability density functions. The Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances By enhancing the contrast of an image in such a way might amplify noise if present and produce worse results. A noise adaptive fuzzy switching median filter is used for salt-and-pepper noise removal. It is able to suppress high-density of salt-and-pepper noise, at the same time preserving fine image details, edges and textures.

Reference :

  1. [1] .R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2006.

    [2] Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consumer Electron., vol. 43, no. 1, pp. 1–8, Feb 1997.

    [3] S.-D. Chen and A. Ramli, “Contrast enhancement using recursive Mean-Separate histogram equalization for scalable brightness preservation,” IEEE Trans. on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, Nov. 2003.

    [4] Kenny Kal Vin Toh and Nor Ashidi Mat Isa, “Noise Adaptive Fuzzy Switching Median Filterfor Salt-andPepper Noise Reduction,” IEEE Signal Processing Letters, vol. 17, no. 3, march 2010.

    [5] Turgay Celik and Tardi Tjahjadi “Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling,” IEEE Transactions On Image Processing, Vol. 21, No. 1, January 2012

    [6] M. Figueiredo and A. Jain, “Unsupervised learning of finite mixture models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 381–396, Mar. 2002.

    [7] [Online].Available:http://www.imagecompression.info/t est_image.

     


  2. [1] .R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2006.

    [2] Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consumer Electron., vol. 43, no. 1, pp. 1–8, Feb 1997.

    [3] S.-D. Chen and A. Ramli, “Contrast enhancement using recursive Mean-Separate histogram equalization for scalable brightness preservation,” IEEE Trans. on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, Nov. 2003.

    [4] Kenny Kal Vin Toh and Nor Ashidi Mat Isa, “Noise Adaptive Fuzzy Switching Median Filterfor Salt-andPepper Noise Reduction,” IEEE Signal Processing Letters, vol. 17, no. 3, march 2010.

    [5] Turgay Celik and Tardi Tjahjadi “Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling,” IEEE Transactions On Image Processing, Vol. 21, No. 1, January 2012.

    [6] M. Figueiredo and A. Jain, “Unsupervised learning of finite mixture models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 381–396, Mar. 2002.

    [7] [Online].Available:http://www.imagecompression.info/t est_image.


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DOI : 10.36647/ijerece/0000