Image restoration using anisotropic multivariate shrinkage function in contourlet domain Online publication date: Thu, 28-Apr-2016
by Huimin Lu; Yujie Li; Shiyuan Yang; Seiichi Serikawa
International Journal of Computational Science and Engineering (IJCSE), Vol. 12, No. 2/3, 2016
Abstract: We describe a method for removing non-Gaussian image noise in natural images, underwater images and biomedical images, based on a statistical model of the decomposed contourlet coefficients. This method utilises the non-Gaussian multivariate shrinkage (NGMS) probability density function (PDF) to model neighbourhood contourlet coefficients. Then, according to the proposed PDF model, we design a maximum a posteriori (MAP) estimator, which relies on a Bayesian statistics representation for the contourlet coefficients of noisy images. There are three obvious virtues of this method. Firstly, contourlet transform decomposition prior to curvelet transform and wavelet transform by using ellipse sampling grid. Secondly, NGMS model is more effective in presentation of the noisy image contourlet coefficients. Thirdly, the NGMS model takes full account of the correlation between coefficients. Some comparisons with the best available results will be presented in order to illustrate the effectiveness of the proposed method.
Online publication date: Thu, 28-Apr-2016
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