  Denoising of coefficients in a sparse domain (e.g. wavelet) has been
researched extensively because of its simplicity and effectiveness. Literature
mainly has focused on designing the best global threshold. However, this paper
proposes a new denoising method using bivariate shrinkage function in framelet
domain. In the proposed method, maximum aposteriori probability is used for
estimate of the denoised coefficient and non-Gaussian bivariate function is
applied to model the statistics of framelet coefficients. For every framelet
coefficient, there is a corresponding threshold depending on the local
statistics of framelet coefficients. Experimental results show that using
bivariate shrinkage function in framelet domain yields significantly superior
image quality and higher PSNR than some well-known denoising methods.
