By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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