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    Abstract
2012 (Vol. 4, Issue: 18)
Article Information:

Bayesian Image Denoising by Local Singularity Detection

Yanqiu Cui, Tao Zhang and Shuang Xu
Corresponding Author:  Yanqiu Cui 

Key words:  Image denoising, singularity, wavelet transform, , , ,
Vol. 4 , (18): 3339-3343
Submitted Accepted Published
March 03, 2012 May 18, 2012 September 15, 2012
Abstract:

In this study, we present a wavelet-based method for removing noise from images and a Bayesian shrinkage factor was derived to estimate noise-free wavelet coefficients. This method took into account dependencies between wavelet coefficients. The interscale dependencies were measured from the local singularity and a conditional probability model was proposed. The intrascale dependencies were measured from the spatial clustering properties and a prior probability model was used. Based on these models in a Bayesian framework, each coefficient was modified separately. Experimental results demonstrate this method improves the denoising performance and preserves the details of the image.
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  Cite this Reference:
Yanqiu Cui, Tao Zhang and Shuang Xu, 2012. Bayesian Image Denoising by Local Singularity Detection.  Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3339-3343.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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