Multi-channel visual polynomial computing from zero-crossings as compressed image data

The neural computing scheme of image reconstruction by the human visual system has been modeled by multi-scale zero-crossings as unique representations of bandlimited polynomial functions. The exact analytical development of such a model and its computer simulation are quite complex tasks. We propose a novel scheme for optical implementation of image reconstruction by synthesizing optical filters involving multiple orthogonal channels. Alternatively the zero crossing operator, i.e. the LOG (Laplacian of Gaussian) operator can also be implemented in a specially designed associative network. A combination of optical implementation and computer simulation of this image reconstruction model may provide exciting insight into the neural mechanisms in the human visual system as well as lead to the development of a real time hybrid signal processing system.