Robust Blind Separation of Statistically Dependent Sources using Dual Tree Wavelets

Blind source separation (BSS) problem is commonly solved by means of independent component analysis (ICA) assuming statistically independent and non-Gaussian sources. The strict independence assumption can be relaxed to existence of subbands where signals are less dependent. In this paper, we use dual tree complex wavelets for the subband decomposition of observed signals and small cummulant based approximation of mutual information for finding the most independent subband(s). We compare the proposed method to previously reported shift invariant and decimated wavelet packet based approach, as well as to innovations based approach. We found proposed dual tree wavelets scheme as an efficient and robust solution of the BSS problem of statistically dependent sources. One important application of the proposed method is related to unsupervised segmentation of medical and remotely sensed multispectral images.