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International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
Xie Yi
Xie Yi
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ASME Press
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This paper proposes a comparative study of different noise removel techniques and wavelet analysis for image denoising. The performance of these techniques was investigated using an image test Lena and real-life images obtained by the Scanning Electron Microscope (SEM) with a set of predefined noise levels. The performance of these techniques was carried out with respect to two quantitative measures: Peak signal-to-noise ration (PSNR), and mean Square Error (MSE). Experimental results on Lena image and image of SEM are compared with various denoising techniques such as mean filtering; median filtering, Wiener filtering and discrete wavelet transform (DWT) analysis in conjunction with neighbour threshold. The results of the comparative study show that this method presents the best performance among all denoising techniques.

Key Words
1 Introduction
2. Theoretical Background and Method
2. Neighshrink Method
3. Results and Discusssion
4. Conclusion
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