The human vision process is presented as a linear, decentralized, continuous time system. This model is then transformed into a representation using neural networks and incorporating non-linearities. First, each cell in the human retina is analyzed to find its specific function. Second, the individual cells are grouped into interconnected systems. Third, a neural network is implemented to model each subsystem. The neural network architecture selected is the Three-Neuron Controller (TNC). Finally, the interconnections are also represented as a neural network, with the nodes being composed of the subsystems. Two major results are presented. First, the overall image quality is improved with the incorporation of neural networks. Second, better edge enhancement is achieved. The edge enhancement is a product of the interconnections between the subsystems.