Abstract: Cornsweet and Yellott invented the nonlinear intensity dependent spread (IDS) filter based on the human visual system. They showed that this filter shares certain characteristics with the human visual system such as Mach bands, Weber's law, and Ricco's law, which account for the bandpass characteristic, brightness constancy, and trade off between stability and resolution. Up to now, because of its nonlinearity and mathematical complexity, the study of its response has been limited to simple images such as step edges or sinusoidal gratings. Also, no good inverse models have been introduced to allow this filter to possibly be used for image compression. In this paper we provide a more complete model for the IDS filter and its inverse and show that for all circularly symmetric spread functions, the bandpass characteristic of the IDS filter can be modeled as spatial summation of spatially varying high pass filter, and that the high pass filter can be modeled as the Laplacian of a low pass filter. We then show that the image can be recovered by inverting the effects of the Laplacian, followed by a deblurring stage and then computing the reciprocal of the result. !12
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