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Application of Fuzzy Filter for Image Deblurring


Affiliations
1 Department of Computer Science, Government Arts College for Women, Salem-8, India
     

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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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  • Application of Fuzzy Filter for Image Deblurring

Abstract Views: 216  |  PDF Views: 3

Authors

S. Lakshmi Prabha
Department of Computer Science, Government Arts College for Women, Salem-8, India

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.