Trilobite-inspired neural nanophotonic light-field camera with extreme depth-of-field

Inspired by the optical structure of bifocal compound eyes, the authors demonstrate a nanophotonic light-field camera with large depth of field. By using a spin-multiplexed bifocal metalens array and neural network-based reconstruction, they capture high-resolution images at centimeter to kilometer scale. A unique bifocal compound eye visual system found in the now extinct trilobite, Dalmanitina socialis, may enable them to be sensitive to the light-field information and simultaneously perceive both close and distant objects in the environment. Here, inspired by the optical structure of their eyes, we demonstrate a nanophotonic light-field camera incorporating a spin-multiplexed bifocal metalens array capable of capturing high-resolution light-field images over a record depth-of-field ranging from centimeter to kilometer scale, simultaneously enabling macro and telephoto modes in a snapshot imaging. By leveraging a multi-scale convolutional neural network-based reconstruction algorithm, optical aberrations induced by the metalens are eliminated, thereby significantly relaxing the design and performance limitations on metasurface optics. The elegant integration of nanophotonic technology with computational photography achieved here is expected to aid development of future high-performance imaging systems.

Automated summary

Inspired by the optical structure of trilobite compound eyes, the authors demonstrate a nanophotonic light-field camera capable of capturing high-resolution images at various scales by using a spin-multiplexed bifocal metalens array and neural network-based reconstruction. By integrating nanophotonic technology with computational photography, this camera eliminates optical aberrations induced by the metalens and provides potential for future high-performance imaging systems.