Normalized Component Analysis and the Statistics of Natural Scenes
Eero Simoncelli
I present a simple statistical model for images in the wavelet
transform domain. The model characterizes the joint densities of
coefficients at adjacent spatial locations, adjacent orientations, and
adjacent spatial scales. The model accounts for the statistics of a
wide variety of images. The model also suggests a nonlinear form of
optimal representation, which I call ``normalized component
analysis'', in which each wavelet coefficient is divided by a linear
combination of coefficients corresponding to basis functions at
adjacent locations, orientations and scales. These statistical
results provide theoretical motivation for the normalization models
that have recently become popular in modeling the behavior of striate
cortical neurons. In addition, I'll demonstrate the power of the
decomposition for applications such as image compression, enhancement
and synthesis.
Back to the Natural Scenes Meeting Agenda.