  In this work, we propose a non-iterative Gaussian transformation strategy
based on copula function, which doesn't require some commonly seen restrictive
assumptions in the previous studies such as the elliptically symmetric
distribution assumption and the linear independent component analysis
assumption. Theoretical properties guarantee the proposed strategy can exactly
transfer any random variable vector with a continuous multivariate distribution
to a variable vector that follows a multivariate Gaussian distribution.
Simulation studies also demonstrate the outperformance of such a strategy
compared to some other methods like Box-Cox Gaussianization and radial
Gaussianization. An application for probability density estimation for image
synthesis is also shown.
