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Wavelet-based multiscale level-set curve evolution and adaptive statistical analysis for image denoising

[+] Author Affiliations
Junmei Zhong

Cincinnati Children’s Hospital Medical Center, Imaging Research Center, 3333 Burnet Avenue, ML5031, Cincinnati, Ohio 45229

J. Electron. Imaging. 15(4), 043004 (October 30, 2006). doi:10.1117/1.2372784
History: Received May 24, 2005; Revised September 12, 2005; Accepted June 14, 2006; Published October 30, 2006
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The deficiency of the existing level-set-based denoising techniques is that they are sensitive to noise. This is due to that fact that the curvature and gradient measurements in the partial differential equation are very sensitive to noise, and the denoising performance is affected. This work proposes to perform the level-set-based curve evolution on the dyadic wavelet transform domain. The main advantage is that in the dyadic wavelet transform domain, noise has less influence on curvature and gradient measurements as the scale increases. Thus, the edge indicator function value can be directly calculated from the dyadic wavelet coefficients rather than from an external force field by convolving the noisy image with a Gaussian filter. For further reducing the noise at the finest scale where noise is dominant, minimum mean-squared-error (MMSE)-based filtering is performed as the first pass of denoising, followed by performing the level-set curve evolution as the second pass of further denoising and enhancement. Experimental results demonstrate that the proposed algorithm generates state of the art denoising results.

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Citation

Junmei Zhong
"Wavelet-based multiscale level-set curve evolution and adaptive statistical analysis for image denoising", J. Electron. Imaging. 15(4), 043004 (October 30, 2006). ; http://dx.doi.org/10.1117/1.2372784


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