Image reconstruction for the artificial compound eye based on deep learning

The visual system of arthropods, called the compound eye, has distinctive features such as a wide field of view, high-speed motion detection, and infinite depth of field. These features have attracted researchers to build artificial compound eyes. However, the compound eye is limited in spatial resolution by its structural constraints such as the number and size of ommatidia that compose the compound eye. These constraints also can be found in the existing artificial compound eye. In previous work, a design method overcame these limitations and achieved resolution improvements by increasing the acceptance angle of ommatidia and using numerical optimization based on compressive sensing (CS). However, the limitation is that prior information such as a sparsifying basis is needed to solve the numerical optimization problem, and obtaining the solution to this problem is computationally time-consuming. In this paper, we propose a deep learning-based artificial compound eye. The deep learning architecture takes a measurement from the compound eye as input and learns how to reconstruct the original image. The experimental result demonstrates that the proposed deep learning approach provides improved performance in image reconstruction for the artificial compound eye.