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.

中文翻译：

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具有计算复眼的紧凑型仿生相机

人们对受节肢动物启发的人工复眼（CE）的进化和改进越来越感兴趣。然而，现有的CE相机由于与商用CMOS相机不兼容而存在散焦问题。受南美虾 CE 的启发，我们报告了一款紧凑型仿生相机，可实现宽视场 (FOV)、高分辨率成像和灵敏的 3D 移动轨迹重建。为了克服散焦问题，提出了一种具有距离调节的深度学习架构，无需任何硬件或复杂的前端设计即可实现大范围清晰成像，大大降低了系统复杂度和尺寸。该架构由Unet和Pyramid的变体——多尺度注意力组成，设计了短距离、中距离和长距离调节。与当前竞争的知名模型相比，我们的方法至少领先2 dB。在这里，我们描述了具有 271 个小眼、重量为 5.4 g、面积为 3 × 3 cm 的高分辨率计算 CE 相机2厚度为5mm，实现了CE与商用CMOS的兼容和集成。实验结果表明，该计算CE相机在增强分辨率和灵敏的3D实时运动轨迹重建方面具有竞争优势。这种紧凑型相机在医学内窥镜、全景成像和视觉机器人等纳米光学领域具有广阔的应用前景。