Monochrome and color image denoising using neighboring dependency and data correlation
Dongwook Cho, Tien Dai Bui · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
In this paper, two approaches for image denoising that take advantages of neighboring dependency in the wavelet domain are studied. The first approach is to take into account the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. The second is based on multivariate statistical modeling. The estimation of the clean coefficients is obtained by a general rule using Bayesian approach. Various estimation expressions can be obtained by a priori probability distribution, called multivariate generalized Gaussian distribution (MGGD). The experimental results show that both of our methods give comparatively higher peak signal to noise ratio (PSNR) as well as little visual artifact for monochrome images. In addition, we extend our approaches to a denoising algorithm for color image that has multiple color components. The proposed color denoising algorithm is a framework to consider the correlations between color components yet using the existing monochrome denoising method without modification. Denoising results in this framework give noticeable better improvement than in the case when the correlation between color components is not considered.