| Abstract
Regularization constraints are necessary
in inverse problems such as image restoration, optical flow computation
or shape from shading to avoid the singularities in the solution. Conventional
regularization techniques are based on some a priori knowledge of the solution:
usually, the solution is assumed to be smooth according to simple statistical
image or motion models.
Using the fact that human visual perception
is adapted to the statistics of natural images and sequences, the class
of regularization functionals proposed in this work are not based on an
image model but on a model of the human visual system. In particular, the
current non-linear model of early human visual processing is used to obtain
locally adaptive regularization functionals for image restoration without
any a priori assumption on the image or the noise. The results show that
these functionals constitute a valid alternative to those based on the
local autocorrelation of the image.
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