  Whereas conventional state-of-the-art image processing systems of recording
and output devices almost exclusively utilize square arranged methods,
biological models, however, suggest an alternative, evolutionarily-based
structure. Inspired by the human visual perception system, hexagonal image
processing in the context of machine learning offers a number of key advantages
that can benefit both researchers and users alike. The hexagonal deep learning
framework Hexnet leveraged in this contribution serves therefore the generation
of hexagonal images by utilizing hexagonal deep neural networks (H-DNN). As the
results of our created test environment show, the proposed models can surpass
current approaches of conventional image generation. While resulting in a
reduction of the models' complexity in the form of trainable parameters, they
furthermore allow an increase of test rates in comparison to their square
counterparts.
