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Abstract
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Article Information:
Bayesian Image Denoising by Local Singularity Detection
Yanqiu Cui, Tao Zhang and Shuang Xu
Corresponding Author: Yanqiu Cui
Submitted: March 03, 2012
Accepted: May 18, 2012
Published: September 15, 2012 |
Abstract:
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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.
Key words: Image denoising, singularity, wavelet transform, , , ,
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Abstract
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Cite this Reference:
Yanqiu Cui, Tao Zhang and Shuang Xu, . Bayesian Image Denoising by Local Singularity Detection. Research Journal of Applied Sciences, Engineering and Technology, (18): 3339-3343.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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