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A New Approach to Reduce Guassian Noise in Images using Fuzzy Logic

Janardhan C N, Mohamed Rafi

Abstract


The quality of an image in any image processing algorithm plays a vital role as the quality of image is a primary thing. There are several techniques employed to enhance the quality of the image. Few techniques are applied to all images irrespective of the noise and few are employed to reduce particular type of noise present in the images. In this paper a new method is used for the noise reduction of images contaminated with Gaussian noise by using fuzzy image filter wherein the fuzzy derivatives are derived with the help of fuzzy rules which are then used for the fuzzy image smoothing technique to reduce the noise that also uses the values of membership functions. Here the technique of smoothing is applied to de-noise the image, later the de-fuzzification techniques are applied to get the de-noised image. The result shows the most efficient filter to remove the white Gaussian noise. Marking the maximum and minimum performance of filter for different images helps in designing and comparing the new filters which give better results than the existing filters. 

Keywords


Gaussian Noise, Image Fuzzification, Fuzzy smoothing, Fuzzy Derivatives, Image De-Noising, Fuzzy Rules, Fuzzy Inference System

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References


Kwan B. Y. M., Kwan H. K., Impulse Noise Reduction in Brain Magnetic Resonance Imaging using Fuzzy Filters, World Acad. Sci. Eng. Technol. 2011; 60: 1194–1197p.

Gonzalez R., Wintz P. Digital Image Processing, Addison – Wesley, Boston, MA, USA, 1987.

Fuzzy Logic Tool Box User Guide Matlab (R2009b).

Jayaram B., Fuzzy Inference System based Contrast Enhancement, EUSFLAT-LFA 2011

Hari Krishnanand M., Viswanathan R. A New Concept of Reduction of Gaussian Noise in Images Based on Fuzzy Logic. Appl. Math. Sci. 2013; 7(12): 595–602p.

Sivanandam S.N., Deepa S. N. Principles of Soft Computing. John Wiley & Sons, Inc, 2009.

Mario. I., Chacon. M, Fuzzy Logic for Image Processing, Advanced Fuzzy Logic Techniques in Industrial Applications, 2006.

Andrews H.C., Tescher A.G., Kruger R.P., Image Processing by Digital Computer, IEEE Spectrum, July 1972; 9: 20–32p.

Lee C.S., Kuo V.H., Yu P.T. Weighted Fuzzy Mean Filters for Image Processing, Fuzzy Sets. Syst. 1997; 89: 157–180p.

Kare E., Nachtegael M., Eds., Fuzzy Techniques in Image Processing, New York: Springer– Verlag 2000; 52: Studies in Fuzziness and Soft Computing.

D. Van De. Ville, Nachtegael M., Weken D.V., et al. Noise Reduction by Fuzzy Image Filtering, IEEE T. Fuzzy Syst. August-2003; 11(4): 429–436p.


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