Removal of Correlated Noise by Modeling Spatial Correlations and Interscale Dependencies in the Complex Wavelet Domain

We develop a new vector-based shrinkage rule, based on the concept of "signal of interest", for the removal of correlated noise. The multivariate Bessel K form density is used for modeling the spatial correlations between complex wavelet coefficients. The interscale dependencies between the coefficients are captured using a hidden Markov tree model. The combined spatial and interscale model gives improvements over recently proposed hidden Markov models for white noise. The results show that correlated noise is suppressed well while image details are being preserved.