Perceptual regularization functionals for natural image restoration
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 nonlinear 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.