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


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In the expanding fields of mobile technology and augmented reality, there is a growing demand for compact, high-fidelity spectral imaging systems. Traditional spectral imaging techniques face limitations due to their size and complexity. Diffractive optical elements (DOEs), although helpful in reducing size, primarily modulate the phase of light. Here, an end-to-end computational spectral imaging framework based on polarization-multiplexed metalens is introduced. 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. Integrating polarization-multiplexed metalens with advanced neural network processing, this study introduces a novel computational spectral imaging framework. Through joint optimization of the front-end metalens and the back-end neural network, high fidelity of spectral image recovery is achieved while ensuring miniaturization. This innovation is pivotal for enhancing mobile technology and augmented reality applications. image
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Key words
computational spectral imaging,joint optimization,metasurfaces
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