Subband adaptive image denoising via bivariate shrinkage

Abstract

It is well known that the wavelet coefficients of natural images have significant statistical dependencies. To model the non-Gaussian nature of these statistics, a new bivariate PDF is proposed in this paper and applied to the image denoising problem. For this purpose, the corresponding new bivariate shrinkage function is derived using the MAP estimator. Using this function, a subband dependent data-driven system is described and applied to both orthogonal and dual-tree complex wavelet coefficients. Also, some comparisons to the other effective data-driven techniques are given.