Compact biologically inspired camera with computational compound eye

Abstract

The growing interests have been witnessed in the evolution and improvement of artificial compound eyes (CE) inspired by arthropods. However, the existing CE cameras are suffering from a defocusing problem due to the incompatibility with commercial CMOS cameras. Inspired by the CEs of South American Shrimps, we report a compact biologically inspired camera that enables wide-field-of-view (FOV), high-resolution imaging and sensitive 3D moving trajectory reconstruction. To overcome the defocusing problem, a deep learning architecture with distance regulation is proposed to achieve wide-range-clear imaging, without any hardware or complex front-end design, which greatly reduces system complexity and size. The architecture is composed of a variant of Unet and Pyramid-multi-scale attention, with designed short, middle and long distance regulation. Compared to the current competitive well-known models, our method is at least 2 dB ahead. Here we describe the high-resolution computational-CE camera with 271 ommatidia, with a weight of 5.4 g an area of 3 × 3 cm2 and 5-mm thickness, which achieves compatibility and integration of CE with commercial CMOS. The experimental result illustrates this computational-CE camera has competitive advantages in enhanced resolution and sensitive 3D live moving trajectory reconstruction. The compact camera has promising applications in nano-optics fields such as medical endoscopy, panoramic imaging and vision robotics.

Keywords

• compound eye
• imaging system
• deep learning
• integrated optics