Density Modeling of Images using a Generalized Normalization Transformation

Understand

We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images.

• The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant.
• We optimize the parameters of the full transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses.
• The optimized transformation substantially Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than alternative methods including ICA and radial Gaussianization.