Current models of primary visual cortex (V1) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behavior of neurons. The nonlinear stage has been modeled as a divisive normalization in which each input linear response is half-rectified, squared and then divided by a weighted sum of half-rectified and squared linear responses in a certain neighborhood. Recently, Simoncelli and colleagues have suggested that this normalization reduces the statistical dependence of neuron responses. In this communication, we present an efficient implementation of these ideas as a practical image representation, and suggest some applications. The linear stage is implemented as a four-level orthogonal wavelet decomposition based on Daubechies filters, and the nonlinear normalization stage uses an improved version of Simoncelli's scheme. The normalization parameters are adapted to minimize statistical dependence between the output responses, so that the resulting representation consists of a set of statistically independent features or visual events. Since both linear and non-linear transforms applied can be inverted, this representation can be highly useful in different applications.