The model also suggests a nonlinear method of removing these dependencies, which I call ``normalized component analysis'', in which each wavelet coefficient is divided by a linear combination of coefficient magnitudes at adjacent locations, orientations and scales. This analysis provides a theoretical justification for recent divisive normalization models of striate visual cortex. Furthermore, the statistical measurements may be used to determine the weights that are used in computing the normalization signal. The resulting model makes specific predictions regarding non-specific suppression and adaptation behaviors of cortical neurons, and thus offer the opportunity to test directly (through physiological measurements) the ecological hypothesis that visual neural computations are optimally matched to the statistics of images.