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Abstract:
The two most prominent features of early visual
processing are orientation selective filtering and contrast gain
control. While the effect of orientation selectivity can be assessed
within in a linear model, contrast gain control is an inherently
nonlinear computation. Here we employ the class of L_p
elliptically contoured distributions to investigate the extent to
which the two features, orientation selectivity and contrast gain
control, are suited to model the statistics of natural images.
Within this model we find that contrast gain control can play a
significant role for the removal of redundancies in natural images.
Orientation selectivity, in contrast, has only a very limited
potential for linear redundancy reduction.