Multivariate Statistical Approach for Image Denoising
Dongwook Cho, Tien Dai Bui · 2006
In this paper, we derive the general estimation rule in the wavelet domain to obtain the denoised coefficients from the noisy image based on the multivariate statistical theory. We define a parametric multivariate generalized Gaussian distribution (MGGD) model which closely fits the actual distribution of wavelet coefficients in clean natural images. The multivariate model makes it possible to exploit the dependency between the estimated wavelet coefficients and their neighbours or other coefficients in different subbands. Also it can be shown that some of the existing methods based on statistical modeling are subsets of our multivariate approach. Our method could achieve high quality image denoising. Among the comparable image denoising methods using the same type of wavelet (esp. Daubechies 8) filter, our results produce comparatively higher peak signal to noise ratio (PSNR).