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Proceedings Paper

Local bivariate Cauchy distribution for video denoising in 3D complex wavelet domain
Author(s): Hossein Rabbani; Mansur Vafadust; Ivan Selesnick
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Paper Abstract

In this paper, we present a new video denoising algorithm using bivariate Cauchy probability density function (pdf) with local scaling factor for distribution of wavelet coefficients in each subband. The bivariate pdf takes into account the statistical dependency among wavelet coefficients and the local scaling factor model the empirically observed correlation between the coefficient amplitudes. Using maximum a posteriori (MAP) estimator and minimum mean squared estimator (MMSE), we describe two methods for video denoising which rely on the bivariate Cauchy random variables with high local correlation. Because separate 3-D transforms, such as ordinary 3-D wavelet transforms (DWT), have artifacts that degrade their performance for denoising, we implement our algorithms in 3-D complex wavelet transform (DCWT) domain. In addition, we use our denoising algorithm in 2-D DCWT domain, where the 2-D transform is applied to each frame individually. The simulation results show that our denoising algorithms achieve better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).

Paper Details

Date Published: 24 September 2007
PDF: 10 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66962G (24 September 2007); doi: 10.1117/12.740040
Show Author Affiliations
Hossein Rabbani, Amirkabir Univ. of Technology (Iran)
Mansur Vafadust, Amirkabir Univ. of Technology (Iran)
Ivan Selesnick, Polytechnic Univ. (United States)


Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)

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