Neural-Optic Co-Designed Polarization-Multiplexed Metalens for Compact Computational Spectral Imaging

TL;DR

This paper presents a novel polarization-multiplexed metalens integrated with neural networks for compact, high-fidelity spectral imaging, enabling joint optimization of hardware and software for miniaturized systems.

Contribution

It introduces an end-to-end differentiable framework combining a polarization-multiplexed metalens with neural networks for spectral imaging, advancing miniaturization and performance.

Findings

High spatial-spectral reconstruction accuracy

Effective joint optimization of metalens and neural network

Potential for miniaturized spectral imaging systems

Abstract

As the realm of spectral imaging applications extends its reach into the domains of mobile technology and augmented reality, the demands for compact yet high-fidelity systems become increasingly pronounced. Conventional methodologies, exemplified by coded aperture snapshot spectral imaging systems, are significantly limited by their cumbersome physical dimensions and form factors. To address this inherent challenge, diffractive optical elements (DOEs) have been repeatedly employed as a means to mitigate issues related to the bulky nature of these systems. Nonetheless, it's essential to note that the capabilities of DOEs primarily revolve around the modulation of the phase of light. Here, we introduce an end-to-end computational spectral imaging framework based on a polarization-multiplexed metalens. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high-fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial-spectral reconstruction performance, underscoring the efficacy of this system in practical, real-world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.