Nonlinear extraction of independent components of natural images using radial gaussianization.

ABSTRACT: We consider the problem of efficiently encoding a signal by transforming it to a new representation whose components are statistically independent. A widely studied linear solution, known as independent component analysis (ICA), exists for the case when the signal is generated as a linear transformation of independent nongaussian sources. Here, we examine a complementary case, in which the source is nongaussian and elliptically symmetric. In this case, no invertible linear transform suffices to decompose the signal into independent components, but we show that a simple nonlinear transformation, which we call radial gaussianization (RG), is able to remove all dependencies. We then examine this methodology in the context of natural image statistics. We first show that distributions of spatia

SUBMITTER: Lyu S 

PROVIDER: S-EPMC3120963 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature