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Using bivariate Gaussian distribution for image denoising in the 2-D complex wavelet domain

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4 Author(s)
Rekabdar, A. ; Dept. of Math. & Comput. Sci., Amirkabir Univ. of Technol., Tehran, Iran ; Khayat, O. ; Khatib, N. ; Aminghafari, M.

Within this framework we describe a novel technique for removing noise from digital noisy images, based on the modeling of wavelet coefficient with bivariate normal distribution and statistical calculation. A method for image denoising is presented in this paper to maximize a posterior density function (MAP) estimator using a bivariate normal random variable. We use our denoising algorithm in 2-D complex wavelet domain comparing with soft and hard thresholding method of stationary wavelet analysis tool (2-D SWT). Despite the simplicity of our method in its implementation, our denoising results achieves better performance than the other mentioned methods both visually and in terms of peak signal-to-noise ratio (PSNR).

Published in:

Machine Vision and Image Processing (MVIP), 2010 6th Iranian

Date of Conference:

27-28 Oct. 2010

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