ICA based on asymmetry

In this paper we present a competitive approach to ICA based on the Split Gaussian distribution, which is well adapted to asymmetric data. Consequently, we obtain a method which works better than the classical approaches, especially in the case when the underlying density is not symmetric, which is a typical situation in the color distribution in images.