The current camera has made a huge progress in the sensor resolution and the lowluminance performance. However, we are still far from having an optimal camera as powerful as our eye is. The study of the evolution process of our visual system indicates attention to two major issues: the form and the density of the sensor. High contrast and optimal sampling properties of our visual spatial arrangement are related directly to the densely hexagonal form. In this paper, we propose a novel software-based method to create images on a compact dense hexagonal grid, derived from a simulated square sensor array by a virtual increase of the fill factor and a half a pixel shifting. After that, the orbit functions are proposed for a hexagonal image processing. The results show it is possible to achieve an image processing in the orbit domain and the generated hexagonal images are superior, in detection of curvature edges, to the square images. We believe that the orbit domain image processing has a great potential to be the standard processing for hexagonal images.
Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework
