A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
What is the goal of sensory coding?
Neural Computation
Modeling surround suppression in V1 neurons with a statistically-derived normalization model
Proceedings of the 1998 conference on Advances in neural information processing systems II
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
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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 squared and then divided by a weighted sum of squared linear responses in a certain neighborhood. In this communication, we show that such a scheme permits an efficient coding of natural image features. In our case, the linear stage is implemented as a four-level Daubechies decomposition, and the nonlinear normalization parameters are determined from the statistics of natural images under the hypothesis that sensory systems are adapted to signals to which they are exposed. In particular, we fix the weights of the divisive normalization to the mutual information of the corresponding pair of linear coefficients. This nonlinear process extracts significant, statistically independent, visual events in the image.