Bio-inspired computer fovea model based on hexagonal-type cellular neural network

Abstract

For decades, numerous scientists have examined the following questions: "How do humans see the world?" and "How do humans experience vision?" To answer these questions, this study proposes a computer fovea model based on hexagonal-type cellular neural network (hCNN). Certain biological mechanisms of a retina can be simulated using an in-state-of-art architecture named CNN. Those biological mechanisms include the behaviors of the photoreceptors, horizontal cells, ganglions, and bipolar cells, and their co-operations in the retina. Through investigating the model and the abilities of the CNN, various properties of the human vision system can be simulated. The human visual system possesses numerous interesting properties, which provide natural methods of enhancing visual information. Various visual information enhancing algorithms can be developed using these properties and the proposed model. The proposed algorithms include color constancy, image sharpness, and some others. This study also discusses how the proposed model works for video enhancement and demonstrates it experimentally.