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Application of Fuzzy Filter for Image Deblurring

Dr.S. Lakshmi Prabha

Abstract


Nonlinear techniques have recently assumed significance as they are able to suppress Gaussian noise which is also called as white additive noise to preserve important signal elements such as edges and fine details and eliminate degradations occurring during signal formation or transmission through nonlinear channels. Among nonlinear techniques, the fuzzy logic based approaches are important as they are capable of reasoning with vague and uncertain information. This paper presents a new fuzzy filter for suppressing noise in lena image and satellite image to show the feasibility of the proposed noise reduction using Fuzzy filter approach and compare it with the existing Mean, Median Filter and Non-Local means Algorithm. This filtering method is more efficient to remove the noise for low noise levels.

Keywords


Gaussian Noise, Fuzzy Logic, Image Processing, Membership Function

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References


Alvarez. L, Lions. P.L and Morel. J.M,” Image selective smoothing and edge detection by nonlinear diffusion “, Journal of numerical analysis, 29:845-866, 1992.

Arakawa “Median filter based on fuzzy rules and its application to image restoration”, fuzzy sets system, pp 3-13, 1996.

Buades.A, Coll. B and Morel.J ,”On image denoising methods,” Technical Report ,CMLA,2004.

Baudes.A, Coll. B and Morel J.M, A non – local algorithm for image denoising, computer vision and pattern recognition, CVPR 2005, IEEE conference, volume 2, PP60-65, 20-25 June 2005.

Lee C.S., Kuo V.H., Yu P.T., Weighted Fuzzy mean filters for image processing, fuzzy sets and systems 89 ( 1997) 157 -180

Menhardt.W,”Iconic Fuzzy Sets for Image Segmentation, NATO Advanced Study Institute on the Formation Handling and Evaluation of Medical Images.Portugal,“1988.

Nachtegael. M, E.E. Kerre, Decomposing and constructing Fuzzy morphological operations over alpha – cuts: Continuous and discrete case, IEEE trans. Fuzzy systems 8 (2000) 615 – 626.

Perona .P and Malik .J, “Scale-space and edge detection using anisotropic diffusion,” in IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.12 , 629-639, 1990.

Pratt W.K,” Digital Image Processing,” John Wiley & sons, Inc., New York, 1978

Ramot.D, Friedman.M ,Langholz.G and Kandel.A,” Complex fuzzy logic,” In IEEE Transactions on Fuzzy Systems, Vol. 11, Issue 4, pp.450 – 461,2003.

Scharcanski.J and Jung. C.R,” An adaptive approach to mammographic image denoising and enhancement,” Computer Graphics and Image Processing. In Proc. IEEE 17th Brazilian Symposium on Computer Graphics and Image Processing, pp.2 – 9,2004.

Schulte.S, Nachtegael. M, De Witte, V, Van der Weken. D and Kerre. E.E,” A fuzzy impulse noise detection and reduction method,” In IEEE Transactions on Image Processing, Vol. 15, Issue 5, pp.1153 - 1162 , 2006

Vese .L.A and Osher .S.J, “Modeling textures with total variation minimization and oscillating patterns in image processing,” Journal of Science and Computation, Vol.19 , 553-572, 2003.

Weickert .J, “Coherence-enhancing diffusion filtering,” in Proc. of the IEEE Int. Conf. on Computer Vision , Vol.31, 111-127, 1999.

Yao Nie and Barner .K.E, “The fuzzy transformation and its applications in image processing,” In IEEE Transactions on Image Processing. Vol. 15, Issue 4, April 2006, pp.910 – 927, April 2006.

Yih-Jen Horng, Shyi-Ming Chen, Yu-Chuan Chang and Chia-Hoang Lee,” A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy reference techniques, “In IEEE Transactions on Fuzzy Systems, Vol.13, Issue 2, pp.216 – 228, April 2005.


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